Journal articles on the topic 'Sequence-based recommender'
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Monti, Diego, Enrico Palumbo, Giuseppe Rizzo, and Maurizio Morisio. "Sequeval: An Offline Evaluation Framework for Sequence-Based Recommender Systems." Information 10, no. 5 (May 10, 2019): 174. http://dx.doi.org/10.3390/info10050174.
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Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare since the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. We provide a mathematical definition of such sequence-based recommenders, a methodology for performing their evaluation, and the implementation details of eight metrics. We report the lessons learned using this framework for assessing the performance of four baselines and two recommender systems based on Conditional Random Fields (CRF) and Recurrent Neural Networks (RNN), considering two different datasets. Sequeval is publicly available and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results.
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Shishehchi, Saman, Nor Azan Mat Zin, and Esmadi Abu Abu Seman. "Ontology-Based Recommender System for a Learning Sequence in Programming Languages." International Journal of Emerging Technologies in Learning (iJET) 16, no. 12 (June 18, 2021): 123. http://dx.doi.org/10.3991/ijet.v16i12.21451.
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The same learning process in educational systems could be boring and time consuming for some learners. This problem arises from the lack of personalized learning sequence for learners with different knowledge level. Recommender systems play an important role in assisting the learners to find suitable learning materials and personalized learning sequence. Use of ontology for knowledge representation in knowledge-based recommender systems would facilitate sharing, reuse and common terminology. Since programming concepts have logical relationships among together so, traditional education systems are more stressful and very time-consuming. This paper aims to propose an ontology based recommender system to present a Personalized Learning Sequence in Programming (PLSP) domain which is depended to learner's knowledge level. A recommender module and, the knowledge base module are integrated together in the proposed framework. The recommender module as the main module in the framework, has three stages which is working based on semantic rules and ontology representation. Evaluation of the system was carried out by comparing the non-recommender system (web-based search) using 32 ICT respondents. Results demonstrate that the participants who used the proposed system spent 1119 seconds to find the suitable learning path in comparison to those who used a non-recommender system (3480 seconds) in the same learning material. It means that learners who follow learning path with PLSP, are more suitable for them. Furthermore, the average mean value of usability test is 4.47, (5 maximum scale) which indicates that the system proved to be useful, was easy to use, and satisfied the users.
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Zhang, Qingsheng, Di Yang, Pengjun Fang, Nannan Liu, and Lu Zhang. "Develop Academic Question Recommender Based on Bayesian Network for Personalizing Student’s Practice." International Journal of Emerging Technologies in Learning (iJET) 15, no. 18 (September 25, 2020): 4. http://dx.doi.org/10.3991/ijet.v15i18.11594.
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Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning. In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components. This question recommender is discussed with theoretical analysis, and designed and implemented in software engineering way. It provides instructor with tools for building knowledge component network and setting question of course. It also makes student personalize practice questions of course. This question recommender is planned to deploy in real learning context for the future validation of how well such question recommendation improves performance and saves practice time for student.
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Fang, Hui, Chongcheng Chen, Yunfei Long, Ge Xu, and Yongqiang Xiao. "DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph." Mathematics 10, no. 9 (April 22, 2022): 1402. http://dx.doi.org/10.3390/math10091402.
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In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.
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Lee, Hea In, Il Young Choi, Hyun Sil Moon, and Jae Kyeong Kim. "A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks." Sustainability 12, no. 3 (January 29, 2020): 969. http://dx.doi.org/10.3390/su12030969.
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A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers’ repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers’ purchasing order and customers’ repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers’ purchasing orders and customers’ repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.
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Wang, Wei, and Longbing Cao. "Interactive Sequential Basket Recommendation by Learning Basket Couplings and Positive/Negative Feedback." ACM Transactions on Information Systems 39, no. 3 (February 23, 2021): 1–26. http://dx.doi.org/10.1145/3444368.
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Sequential recommendation , such as next-basket recommender systems (NBRS), which model users’ sequential behaviors and the relevant context/session, has recently attracted much attention from the research community. Existing session-based NBRS involve session representation and inter-basket relations but ignore their hybrid couplings with the intra-basket items, often producing irrelevant or similar items in the next basket. In addition, they do not predict next-baskets (more than one next basket recommended). Interactive recommendation further involves user feedback on the recommended basket. The existing work on next-item recommendation involves positive feedback on selected items but ignores negative feedback on unselected ones. Here, we introduce a new setting— interactive sequential basket recommendation , which iteratively predicts next baskets by learning the intra-/inter-basket couplings between items and both positive and negative user feedback on recommended baskets. A hierarchical attentive encoder-decoder model (HAEM) continuously recommends next baskets one after another during sequential interactions with users after analyzing the item relations both within a basket and between adjacent sequential baskets (i.e., intra-/inter-basket couplings) and incorporating the user selection and unselection (i.e., positive/negative) feedback on the recommended baskets to refine NBRS. HAEM comprises a basket encoder and a sequence decoder to model intra-/inter-basket couplings and a prediction decoder to sequentially predict next-baskets by interactive feedback-based refinement. Empirical analysis shows that HAEM significantly outperforms the state-of-the-art baselines for NBRS and session-based recommenders for accurate and novel recommendation. We also show the effect of continuously refining sequential basket recommendation by including unselection feedback during interactive recommendation.
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Martínez-López, Francisco J., Irene Esteban-Millat, Ana Argila, and Francisco Rejón-Guardia. "Consumers’ psychological outcomes linked to the use of an online store’s recommendation system." Internet Research 25, no. 4 (August 3, 2015): 562–88. http://dx.doi.org/10.1108/intr-01-2014-0033.
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Purpose – Psychological perspective has been omitted or considered a secondary issue by past studies focused on e-commerce recommendation systems (RS). However, this perspective is key to gaining a better understanding of consumer behaviours when these systems are used to support purchasing processes at online stores. The paper aims to discuss these issues. Design/methodology/approach – The field study consisted of a simulated online shopping process undertaken by a sample of internet users with a recommender system at a real online store (Pixmania). The authors applied rigorous and detailed exploratory and confirmatory factor analyses to assess the empirical validity of the model. Findings – The proposed sequence of psychological outcomes is valid, with the exception of one hypothesized relationship. In particular, satisfaction with an online store’s recommender has a strong influence on a consumer’s willingness to purchase one of the items related to his/her shopping goal. However, this satisfaction has no direct effect on a consumer’s intention to make add-on purchases based on the recommender’s suggestions. On the contrary, the results support the idea that add-on purchases are conditioned by a previous purchase related to the consumer’s initial shopping goal. On the other hand, a consumer’s flow state while shopping improves all his/her psychological outcomes linked to an online store’s recommender. The influence of flow state is particularly interesting when seeking to gain a better understanding of consumers’ unplanned purchases based on the recommender’s suggestions. These findings have important implications for practitioners. Originality/value – This paper discusses in detail and empirically test a set of psychological outcomes that emerge when an e-vendor’s recommender is used to assist a consumer’s shopping process. To the best of the knowledge, this is the first attempt that empirically tests most of the hypothesized relationships within an online store’s RS context.
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Gu, Jiqing, Chao Song, Wenjun Jiang, Xiaomin Wang, and Ming Liu. "Enhancing Personalized Trip Recommendation with Attractive Routes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 662–69. http://dx.doi.org/10.1609/aaai.v34i01.5407.
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Personalized trip recommendation tries to recommend a sequence of point of interests (POIs) for a user. Most of existing studies search POIs only according to the popularity of POIs themselves. In fact, the routes among the POIs also have attractions to visitors, and some of these routes have high popularity. We term this kind of route as Attractive Route (AR), which brings extra user experience. In this paper, we study the attractive routes to improve personalized trip recommendation. To deal with the challenges of discovery and evaluation of ARs, we propose a personalized Trip Recommender with POIs and Attractive Route (TRAR). It discovers the attractive routes based on the popularity and the Gini coefficient of POIs, then it utilizes a gravity model in a category space to estimate the rating scores and preferences of the attractive routes. Based on that, TRAR recommends a trip with ARs to maximize user experience and leverage the tradeoff between the time cost and the user experience. The experimental results show the superiority of TRAR compared with other state-of-the-art methods.
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DELGADO, JOAQUIN, and NAOHIRO ISHII. "MULTI-AGENT LEARNING IN RECOMMENDER SYSTEMS FOR INFORMATION FILTERING ON THE INTERNET." International Journal of Cooperative Information Systems 10, no. 01n02 (March 2001): 81–100. http://dx.doi.org/10.1142/s0218843001000266.
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Recommender Systems (RS), allow users to share information about items they like or dislike and obtain, in a timely fashion, recommendations based on predictions about unseen items (physical or information goods and/or services). In this process, users' preferences are considered to be the learning target functions. We study Agent-based Recommender Systems (ARS) under the scope of online learning in Multi-Agent systems (MAS). This approach models the problem as a pool of independent cooperative predictor agents, one per each user (the masters) in the system, in situations in which each agent (the learners) faces a sequence of trials, with a prediction to make in every step, eventually getting the correct value from its master. Each learner is willing to discover the degree of similarity among the target function of its master and those of other agents' masters (i.e. preference similarity). The agent uses this information for the calculation of its own prediction task, the goal being to make as few mistakes as possible. A simple, yet effective method is introduced in order to construct a compound algorithm for each agent by combining memory-based individual prediction and online weighted-majority voting. We give a theoretical mistake bound for this algorithm that is closely related to the total loss of the best predictor agent in the pool. Finally, we conduct some experiments obtaining results that empirically support these ideas and theories.
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Wu, Shiwen, Yuanxing Zhang, Chengliang Gao, Kaigui Bian, and Bin Cui. "GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network." Data Science and Engineering 5, no. 4 (July 29, 2020): 433–47. http://dx.doi.org/10.1007/s41019-020-00135-z.
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Abstract The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.
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Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–57. http://dx.doi.org/10.1287/mnsc.2018.3127.
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Sometimes we desire change, a break from the same, or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of online media, we show that by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer’s preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts with selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how an individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine the most relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration–diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% additional articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site. This paper was accepted by Chris Forman, information systems.
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Qiu, Ruihong, Zi Huang, Tong Chen, and Hongzhi Yin. "Exploiting Positional Information for Session-Based Recommendation." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–24. http://dx.doi.org/10.1145/3473339.
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For present e-commerce platforms, it is important to accurately predict users’ preference for a timely next-item recommendation. To achieve this goal, session-based recommender systems are developed, which are based on a sequence of the most recent user-item interactions to avoid the influence raised from outdated historical records. Although a session can usually reflect a user’s current preference, a local shift of the user’s intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user’s initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness . Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.
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Falconnet, Antoine, Constantinos K. Coursaris, Joerg Beringer, Wietske Van Osch, Sylvain Sénécal, and Pierre-Majorique Léger. "Improving User Experience with Recommender Systems by Informing the Design of Recommendation Messages." Applied Sciences 13, no. 4 (February 20, 2023): 2706. http://dx.doi.org/10.3390/app13042706.
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Advice-giving systems such as decision support systems and recommender systems (RS) utilize algorithms to provide users with decision support by generating ‘advice’ ranging from tailored alerts for situational exception events to product recommendations based on preferences. Related extant research of user perceptions and behaviors has predominantly taken a system-level view, whereas limited attention has been given to the impact of message design on recommendation acceptance and system use intentions. Here, a comprehensive model was developed and tested to explore the presentation choices (i.e., recommendation message characteristics) that influenced users’ confidence in—and likely acceptance of—recommendations generated by the RS. Our findings indicate that the problem and solution-related information specificity of the recommendation increase both user intention and the actual acceptance of recommendations while decreasing the decision-making time; a shorter decision-making time was also observed when the recommendation was structured in a problem-to-solution sequence. Finally, information specificity was correlated with information sufficiency and transparency, confirming prior research with support for the links between user beliefs, user attitudes, and behavioral intentions. Implications for theory and practice are also discussed.
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Somya, Ramos, Edi Winarko, and Sigit Priyanta. "A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3422–32. http://dx.doi.org/10.11591/eei.v11i6.4021.
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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
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Huang, Ruo, Shelby McIntyre, Meina Song, Haihong E, and Zhonghong Ou. "An Attention-Based Latent Information Extraction Network (ALIEN) for High-Order Feature Interactions." Applied Sciences 10, no. 16 (August 7, 2020): 5468. http://dx.doi.org/10.3390/app10165468.
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One of the primary tasks for commercial recommender systems is to predict the probabilities of users clicking items, e.g., advertisements, music and products. This is because such predictions have a decisive impact on profitability. The classic recommendation algorithm, collaborative filtering (CF), still plays a vital role in many industrial recommender systems. However, although straight CF is good at capturing similar users’ preferences for items based on their past interactions, it lacks regarding (1) modeling the influences of users’ sequential patterns from their individual history interaction sequences and (2) the relevance of users’ and items’ attributes. In this work, we developed an attention-based latent information extraction network (ALIEN) for click-through rate prediction, to integrate (1) implicit user similarity in terms of click patterns (analogous to CF), and (2) modeling the low and high-order feature interactions and (3) historical sequence information. The new model is based on the deep learning, which goes beyond the capabilities of econometric approaches, such as matrix factorization (MF) and k-means. In addition, the approach provides explainability to the recommendation by interpreting the contributions of different features and historical interactions. We have conducted experiments on real-world datasets that demonstrate considerable improvements over strong baselines.
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Yang, Shu, Xiaoxi Liu, and Raymond T. Ng. "ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins." Bioinformatics 36, no. 18 (June 23, 2020): 4797–804. http://dx.doi.org/10.1093/bioinformatics/btaa580.
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Abstract Motivation The interaction between proteins and nucleic acids plays a crucial role in gene regulation and cell function. Determining the binding preferences of nucleic acid-binding proteins (NBPs), namely RNA-binding proteins (RBPs) and transcription factors (TFs), is the key to decipher the protein–nucleic acids interaction code. Today, available NBP binding data from in vivo or in vitro experiments are still limited, which leaves a large portion of NBPs uncovered. Unfortunately, existing computational methods that model the NBP binding preferences are mostly protein specific: they need the experimental data for a specific protein in interest, and thus only focus on experimentally characterized NBPs. The binding preferences of experimentally unexplored NBPs remain largely unknown. Results Here, we introduce ProbeRating, a nucleic acid recommender system that utilizes techniques from deep learning and word embeddings of natural language processing. ProbeRating is developed to predict binding profiles for unexplored or poorly studied NBPs by exploiting their homologs NBPs which currently have available binding data. Requiring only sequence information as input, ProbeRating adapts FastText from Facebook AI Research to extract biological features. It then builds a neural network-based recommender system. We evaluate the performance of ProbeRating on two different tasks: one for RBP and one for TF. As a result, ProbeRating outperforms previous methods on both tasks. The results show that ProbeRating can be a useful tool to study the binding mechanism for the many NBPs that lack direct experimental evidence. and implementation Availability and implementation The source code is freely available at <https://github.com/syang11/ProbeRating>. Supplementary information Supplementary data are available at Bioinformatics online.
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Meduri, Venkata Vamsikrishna, Kanchan Chowdhury, and Mohamed Sarwat. "Evaluation of Machine Learning Algorithms in Predicting the Next SQL Query from the Future." ACM Transactions on Database Systems 46, no. 1 (April 2021): 1–46. http://dx.doi.org/10.1145/3442338.
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Prediction of the next SQL query from the user, given her sequence of queries until the current timestep, during an ongoing interaction session of the user with the database, can help in speculative query processing and increased interactivity. While existing machine learning-- (ML) based approaches use recommender systems to suggest relevant queries to a user, there has been no exhaustive study on applying temporal predictors to predict the next user issued query. In this work, we experimentally compare ML algorithms in predicting the immediate next future query in an interaction workload, given the current user query or the sequence of queries in a user session thus far. As a part of this, we propose the adaptation of two powerful temporal predictors: (a) Recurrent Neural Networks (RNNs) and (b) a Reinforcement Learning approach called Q-Learning that uses Markov Decision Processes. We represent each query as a comprehensive set of fragment embeddings that not only captures the SQL operators, attributes, and relations but also the arithmetic comparison operators and constants that occur in the query. Our experiments on two real-world datasets show the effectiveness of temporal predictors against the baseline recommender systems in predicting the structural fragments in a query w.r.t. both quality and time. Besides showing that RNNs can be used to synthesize novel queries, we find that exact Q-Learning outperforms RNNs despite predicting the next query entirely from the historical query logs.
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Chalyi, S. F., and V. O. Leshchynskyi. "Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System." Bionics of Intelligence 2, no. 95 (December 2, 2020): 21–27. http://dx.doi.org/10.30837/bi.2020.2(95).03.
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The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.
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CASTELLANO, GIOVANNA, CIRO CASTIELLO, DANILO DELL'AGNELLO, ANNA MARIA FANELLI, CORRADO MENCAR, and MARIA ALESSANDRA TORSELLO. "LEARNING FUZZY USER PROFILES FOR RESOURCE RECOMMENDATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 04 (August 2010): 389–410. http://dx.doi.org/10.1142/s0218488510006611.
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Recommender systems are systems capable of assisting users by quickly providing them with relevant resources according to their interests or preferences. The efficacy of a recommender system is strictly connected with the possibility of creating meaningful user profiles, including information about user preferences, interests, goals, usage data and interactive behavior. In particular, analysis of user preferences is important to predict user behaviors and make appropriate recommendations. In this paper, we present a fuzzy framework to represent, learn and update user profiles. The representation of a user profile is based on a structured model of user cognitive states, including a competence profile, a preference profile and an acquaintance profile. The strategy for deriving and updating profiles is to record the sequence of accessed resources by each user, and to update preference profiles accordingly, so as to suggest similar resources at next user accesses. The adaption of the preference profile is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource recommendation. Simulation results are reported to show the effectiveness of the proposed approach.
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Deshmukh, Miss Samradni, and Prof K. R. Ingole. "Graph Based Personalized Travel Recommendation Using Data Mining Technique Collaborative Filtering Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 828–30. http://dx.doi.org/10.22214/ijraset.2022.40739.
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Abstract: The recommendation system has growth choices in recent years. The recommendation system is exist in many applications which gives online travel information for individual travel package. A new model named travel recommendation using data mining techniques which extracts the features like locations, travel seasons of various landscapes. Thus it possesses the material of the travel packages and interests of tourists. Further extending E-TRAST model with the tourist-relation-area season topic model includes relationship with tourists. It includes mining significant tourist locations based on the user search trajectories of users on web and also derives a personalized travel algorithm recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. To suggest personalized POI sequence, first famous routes are stratified as per the similarity between user package and route package. Keywords: Travel package, recommender systems, cocktail, topic modeling, and collaborative filtering
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Burashnikova, Aleksandra, Yury Maximov, Marianne Clausel, Charlotte Laclau, Franck Iutzeler, and Massih-Reza Amini. "Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation." Journal of Artificial Intelligence Research 71 (May 27, 2021): 121–42. http://dx.doi.org/10.1613/jair.1.12562.
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In this paper, we propose a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user. We present two variants of this strategy where model parameters are updated using either the momentum method or a gradient-based approach. To prevent updating the parameters for an abnormally high number of clicks over some targeted items (mainly due to bots), we introduce an upper and a lower threshold on the number of updates for each user. These thresholds are estimated over the distribution of the number of blocks in the training set. They affect the decision of RS by shifting the distribution of items that are shown to the users. Furthermore, we provide a convergence analysis of both algorithms and demonstrate their practical efficiency over six large-scale collections with respect to various ranking measures and computational time.
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Chalyi, Serhii, and Inna Pribylnova. "THE METHOD OF CONSTRUCTING RECOMMENDATIONS ONLINE ON THE TEMPORAL DYNAMICS OF USER INTERESTS USING MULTILAYER GRAPH." EUREKA: Physics and Engineering 3 (May 31, 2019): 13–19. http://dx.doi.org/10.21303/2461-4262.2019.00894.
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The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time.
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George, Gina, and Anisha M. Lal. "A Personalized Approach to Course Recommendation in Higher Education." International Journal on Semantic Web and Information Systems 17, no. 2 (April 2021): 100–114. http://dx.doi.org/10.4018/ijswis.2021040106.
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The selection of elective courses, which best fits the student's personal choice, becomes a challenge, considering the variety of courses available at the higher education level. The traditional recommendation approach often uses collaborative filtering along with sequential pattern mining. Existing recommender systems also use ontology. However, these approaches have several limitations, including lack of availability of ratings at higher education level and lack of personalization based on student attributes. The proposed system intends to overcome these limitations by firstly extracting student personality and profile attributes and thereby generating a set of similar users by utilizing the versatile ontology. Secondly, it predicts courses based on a well-performing sequence prediction algorithm, the compact prediction tree (CPT). The results show that the proposed approach increases the accuracy in terms of precision to a tune of 0.97 and F1 measure to a tune of 0.58 when compared with existing systems which makes the proposed method more suitable for recommending courses.
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Deshmukh, Miss Samradni, and Prof K. R. Ingole. "Implementation Paper on Personalized Travel Recommendation by Mining People Attributes." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1441–45. http://dx.doi.org/10.22214/ijraset.2022.41415.
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Abstract: The recommendation system has growth choices in recent years. The recommendation system is existing in many applications which gives online travel information for individual travel package. A new model named travel recommendation using data mining techniques which extracts the features like locations, travel seasons of various landscapes. Thus, it possesses the material of the travel packages and interests of tourists. Further extending E-TRAST model with the tourist-relation-area season topic model includes relationship with tourists. It includes mining significant tourist locations based on the user search trajectories of users on web and also derives a personalized travel algorithm recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. To suggest personalized POI sequence, first famous routes are stratified as per the similarity between user package and route package. Keywords: Travel package, recommender systems, cocktail, topic modeling, and collaborative filtering
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Wang, Shoujin, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. "Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6259–66. http://dx.doi.org/10.1609/aaai.v34i04.6093.
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Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.
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Dhawan, Sanjeev, Kulvinder Singh, Adrian Rabaea, and Amit Batra. "Session centered Recommendation Utilizing Future Contexts in Social Media." Analele Universitatii "Ovidius" Constanta - Seria Matematica 29, no. 3 (November 1, 2021): 91–104. http://dx.doi.org/10.2478/auom-2021-0036.
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Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender (SRec), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network (CNN) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.
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Wang, Baocheng, and Wentao Cai. "Attention-Enhanced Graph Neural Networks for Session-Based Recommendation." Mathematics 8, no. 9 (September 18, 2020): 1607. http://dx.doi.org/10.3390/math8091607.
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Session-based recommendation, which aims to match user needs with rich resources based on anonymous sessions, nowadays plays a critical role in various online platforms (e.g., media streaming sites, search and e-commerce). Existing recommendation algorithms usually model a session as a sequence or a session graph to model transitions between items. Despite their effectiveness, we would argue that the performance of these methods is still flawed: (1) Using only fixed session item embedding without considering the diversity of users’ interests and target items. (2) For user’s long-term interest, the difficulty of capturing the different priorities for different items accurately. To tackle these defects, we propose a novel model which leverages both the target attentive network and self-attention network to improve the graph-neural-network (GNN)-based recommender. In our model, we first model user’s interaction sequences as session graphs which serves as the input of the GNN, and each node vector involved in session graph can be obtained via the GNN. Next, target attentive network can activates different user interests corresponding to varied target items (i.e., the session embedding learned varies with different target items), which can reveal the relevance between users’ interests and target items. At last, after applying the self-attention mechanism, the different priorities for different items can be captured to improve the precision of the long-term session representation. By using a hybrid of long-term and short-term session representation, we can capture users’ comprehensive interests at multiple levels. Extensive experiments demonstrate the effectiveness of our algorithm on two real-world datasets for session-based recommendation.
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Chalyi, Serhii, and Volodymyr Leshchynskyi. "DEVELOPMENT OF A METHOD FOR THE INTERACTIVE CONSTRUCTION OF EXPLANATIONS IN INTELLIGENT INFORMATION SYSTEMS BASED ON THE PROBABILISTIC APPROACH." Innovative Technologies and Scientific Solutions for Industries, no. 2 (16) (July 6, 2021): 39–45. http://dx.doi.org/10.30837/itssi.2021.16.039.
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Subject: the use of the apparatus of temporal logic and probabilistic approaches to construct an explanation of the results of the work of an intelligent system in order to increase the efficiency of using the solutions and recommendations obtained. Purpose: development of a method for constructing explanations in intelligent systems with the ability to form and evaluate several alternative interpretations of the results of the operation of such a system. Tasks: justification for the use of the black box principle for interactive construction of explanations; development of a pattern explanation model that provides for probabilistic estimation; development of a method of interactive construction of explanations on the basis of the probabilistic approach. Methods: methods of data analysis, methods of system analysis, methods of constructing explanations, models of knowledge representation. Results: A model of the explanation pattern is proposed, which contains temporal regulations reflecting the sequence of user interaction with an intelligent system, which allows the formation of explanations based on a comparison of the actions of the current user and other well-known users. An interactive method for constructing explanations based on a probabilistic approach has been developed; the method uses patterns of user interaction with an intelligent system and contains phases of constructing patterns of explanations and forming explanations using the obtained patterns. The method organizes the received explanations according to the likelihood of use, which makes it possible to form target and alternative explanations for the user. Conclusions: The use of the black box principle for the development of a probabilistic approach to the construction of explanations in intelligent systems has been substantiated. A model of a pattern of explanations based on temporal regulations is proposed. The model reflects the sequence of user interaction with the intelligent system when receiving decisions and recommendations and contains an interaction pattern as part of temporal regulations that have weight, and also determines the likelihood of using the user interaction pattern. An interactive method for constructing explanations has been developed, considering the interaction of the user with the intelligent system. The method includes phases and stages of the formation of regulations and patterns of user interaction with the determination of the probability of their implementation, as well as the ordering of patterns according to the probability of their implementation. The implementation of the method was carried out when constructing explanations for recommender systems.
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Zha, Yongfu, Yongjian Zhang, Zhixin Liu, and Yumin Dong. "Self-Attention Based Time-Rating-Aware Context Recommender System." Computational Intelligence and Neuroscience 2022 (September 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9288902.
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The sequential recommendation can predict the user’s next behavior according to the user’s historical interaction sequence. To better capture users’ preferences, some sequential recommendation models propose time-aware attention networks to capture users’ long-term and short-term intentions. However, although these models have achieved good results, they ignore the influence of users on the rating information of items. We believe that in the sequential recommendation, the user’s displayed feedback (rating) on an item reflects the user’s preference for the item, which directly affects the user’s choice of the next item to a certain extent. In different periods of sequential recommendation, the user’s rating of the item reflects the change in the user’s preference. In this paper, we separately model the time interval of items in the user’s interaction sequence and the ratings of the items in the interaction sequence to obtain temporal context and rating context, respectively. Finally, we exploit the self-attention mechanism to capture the impact of temporal context and rating context on users’ preferences to predict items that users would click next. Experiments on three public benchmark datasets show that our proposed model (SATRAC) outperforms several state-of-the-art methods. The Hit@10 value of the SATRAC model on the three datasets (Movies-1M, Amazon-Movies, Amazon-CDs) increased by 0.73%, 2.73%, and 1.36%, and the NDCG@10 value increased by 5.90%, 3.47%, and 4.59%, respectively.
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Chengyu, Wu, He Chen, and Jiang Lingge. "Spectrum handoff scheme based on recommended channel sensing sequence." China Communications 10, no. 8 (August 2013): 18–26. http://dx.doi.org/10.1109/cc.2013.6633741.
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Huang, Jianfeng, Yuefeng Liu, Yue Chen, and Chen Jia. "Dynamic Recommendation of POI Sequence Responding to Historical Trajectory." ISPRS International Journal of Geo-Information 8, no. 10 (September 30, 2019): 433. http://dx.doi.org/10.3390/ijgi8100433.
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Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.
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Singh, Ninni, and Neelu Jyothi Ahuja. "Bug Model Based Intelligent Recommender System with Exclusive Curriculum Sequencing for Learner-Centric Tutoring." International Journal of Web-Based Learning and Teaching Technologies 14, no. 4 (October 2019): 1–25. http://dx.doi.org/10.4018/ijwltt.2019100101.
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Face to face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's requirements and tutor in a manner that best suits the learner. Reaching this level in an intelligent tutoring system is a challenge even today given the advanced developments in the field. This article focuses on ITS, mimicking a human tutor in terms of providing a curriculum sequence exclusive for the learner. Unsuitable courseware disorients the learner and thus degrades the overall performance. A bug model approach has been used for curriculum design and its re-alignment as per requirements and is demonstrated through a prototype tutoring recommender system, SeisTutor, developed for this purpose. The experimental results indicate an enhanced learning gain through a curriculum recommender approach of SeisTutor as opposed to its absence.
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Dyulicheva, Y. Yu. "The swarm intelligence algorithms and their application for the educational data analysis." Open Education 23, no. 5 (November 2, 2019): 33–43. http://dx.doi.org/10.21686/1818-4243-2019-5-33-43.
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The purpose of the paper is the investigation of the modern approaches and prospects for the application of swarm intelligence algorithms for educational data analysis, as well as the possibility of using of ant algorithm modifications for organizing educational content in adaptive systems for conducting project seminars.Materials and methods. The review of the modern articles on the educational data analysis based on swarm intelligence algorithms is provided; the approaches to solving problem of the optimal learning path construction (optimal organization of the learning objects) based on the algorithm and its modifications taking into account the students’ performance in the process of the optimal learning path construction are investigated; the application of particle swarm optimization and its modification based on Roccio algorithm for the reduction of curse dimension in the problem of the auto classifying questions; the application of ant algorithm, bee colony algorithm and bat algorithm for recommender system construction are studied; the prediction of students’ performance based on particle swarm optimization is researched in the article. The modification of ant algorithm for optimal organization of learning objects at projects seminars is proposed.Results. The modern approaches based on swarm intelligence algorithms to problem solving in educational data analysis are investigated. The various approaches to pheromones updating (their evaporation) when building the optimal learning path based on students’ performance data and search of group with “similar" students are studied; the abilities of the hybrid swarm intelligence algorithms for recommendation construction are investigated.Based on the modification of ant algorithm, the approach to the learning content organization at project seminars with individual preferences and students’ level of basic knowledge is proposed. The python classes are developed: the class for statistical data processing; the classfor modifica -tion of ant algorithm, taking into account the current level of knowledge and interest of student in studying a specific topic at the project seminar; the class for optimal sequence of the project seminars ’ topics for students. The developed classes allow creating the adaptive system that helps first year students with a choice of topics of project seminars.Conclusion. According to the results of the study, we can conclude about the effectiveness of swarm intelligence algorithms usage to solve a wide range of tasks connected with learning content and students’ data analysis in the e-learning systems and perspectives to hybrid approaches development based on swarm intelligence algorithms for realizing the adaptive learning systems on the paradigm of “demand learning".The results can be used to automate the organization of learning content during project seminars for the first-year students, when it is important to understand the basic level of knowledge and students’ interest in learning new technologies.
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Huang, Jianfeng, Yuefeng Liu, Yue Chen, and Chen Jia. "Context-Aware POI Sequence Recommendation with Attention-Based Neural Network." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-126-2019.
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<p><strong>Abstract.</strong> Location-based social networks (LBSNs) is playing an increasingly important role in our daily life, through which users can share their locations and location-related contents at any time. The Location information implicitly expresses user's behaviour preference. Therefore, LBSNs is being widely explored for Point-of-Interest (POI) recommendation in recent years. Most of existing POI recommenders only recommend a single POI, while sometimes successive POI sequence recommendation is more practical. For example, when we travel to a strange city, what we expect is not a single POI recommendation, but a POI sequence recommendation which contains a set of POIs and the order of visiting them. To solve this problem, this paper proposes a novel model called Context-Aware POI Sequence Recommendation (CPSR), which is developed based on an attention-based neural network. Neural network has made a great success in various of field because of its powerful learning ability. Recently, dozens of works has demonstrated that attention mechanism can make the neural network models more reasonable.</p>
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GUEGUEN, Y., J. GARNIER, L. ROBERT, M. LEFRANC, I. MOUGENOT, J. DELORGERIL, M. JANECH, P. GROSS, G. WARR, and B. CUTHBERTSON. "PenBase, the shrimp antimicrobial peptide penaeidin database: Sequence-based classification and recommended nomenclature." Developmental & Comparative Immunology 30, no. 3 (2006): 283–88. http://dx.doi.org/10.1016/j.dci.2005.04.003.
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Zhong, Ying Chun, Guo Chen Huang, Rui Sheng Lin, and Fang Li. "Research on Recommended Pattern of Electric Nursing-Bed Movement Control." Applied Mechanics and Materials 333-335 (July 2013): 1239–46. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1239.
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Aim to the weak intelligence and humanity of current electric nursing-bed control modes, the recommended movement control mode is proposed, which is based on the existing manual, timing and speech control pattern. First, on the basis of accumulating some control data, the Affinity Propagation algorithm (AP algorithm) is employed to cluster in order to acquire the clustering centers, which reflect the prefer movement and corresponding value at special time of the special user. Then, according to the mechanical and electric constraints, some rules are established to adjust the clustering centers. And the nursing-bed movement sequence is obtained, which is logical. Finally, the rationality of the recommended movement sequence is analyzed according to the distribution characteristic of the dataset. The movement sequence that passes the rationality analysis will be recommended to the user and automatically saved as the recommended pattern. The experimental results show that the recommended movement sequence can basically reflect the users habits, which is more intelligent and human than other control modes.
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Mattarelli, Paola, Wilhelm Holzapfel, Charles M. A. P. Franz, Akihito Endo, Giovanna E. Felis, Walter Hammes, Bruno Pot, Leon Dicks, and Franco Dellaglio. "Recommended minimal standards for description of new taxa of the genera Bifidobacterium, Lactobacillus and related genera." International Journal of Systematic and Evolutionary Microbiology 64, Pt_4 (April 1, 2014): 1434–51. http://dx.doi.org/10.1099/ijs.0.060046-0.
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Minimal standards for the description of new cultivable strains that represent novel genera and species belonging to the genera Bifidobacterium , Lactobacillus and related genera are proposed in accordance with Recommendation 30b of the Bacteriological Code (1990 Revision): the description of novel species should be based on phenotypic, genotypic and ecological characteristics to ensure a rich polyphasic characterization. Concerning genotypic characterization, in addition to DNA G+C content (mol%) data, the description should be based on DNA–DNA hybridization (DDH), 16S rRNA gene sequence similarities and at least two housekeeping gene (e.g. hsp60 and recA) sequence similarities. DDH might not be needed if the 16S rRNA gene sequence similarity to the closest known species is lower than 97 %. This proposal has been endorsed by members of the Subcommittee on the Taxonomy of Bifidobacterium , Lactobacillus and related organisms of the International Committee on the Systematics of Prokaryotes.
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Kim, Sangho, Kosuke Sekiyama, and Toshio Fukuda. "User-Adaptive Reconfigurable Interface for In-Vehicle Information Systems." Journal of Robotics and Mechatronics 21, no. 4 (August 20, 2009): 524–32. http://dx.doi.org/10.20965/jrm.2009.p0524.
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To reduce the visual workload produced by information systems and their use during vehicle operation, we propose a user-adaptive, reconfigurable vehicle information interface with a reconfigured navigation sequence and reshaped operating switches (buttons). Our proposed user-adaptive interface consists of user modeling using a hidden Markov model (HMM) and reconfiguration of a preferred navigation sequence. The interface learns a user's navigation patterns based on accumulated navigation sequences. The navigation sequence with the highest likelihood is recommended as preferred among possible sequences, and the navigation sequence is reconfigured omitting intermediate steps. The reconfigurable keypad we propose reflects personal differences because information is customized through the physical reconfiguration of switches corresponding to recommended contents. Information is displayed tactilely and visually together through tactile and visual displays. To analyze the effect of navigation sequence and switch reconfiguration, we conducted experiments using two examples. Experimental results showed that reconfiguring the navigation sequence and switches reduced navigation reaction time.
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D. R. PADMANI, G. R. MARUTHI SANKAR, K. N. AKBARI, M. S. GAJERA, G. S. SUTARIA, and M. K. KHISTARIA. "Sustainable rainfed crop sequence based on rainfall analysis under semi-arid vertisol." Journal of Agrometeorology 11, no. 1 (June 1, 2009): 73–78. http://dx.doi.org/10.54386/jam.v11i1.1227.
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Based on field experiments conducted with 12 crop sequences (groundnut-groundnut, groundnut-cotton, groundnut-castor, groundnut-pearl millet, groundnut-sesame, cotton-cotton, cotton-castor, cotton-pearl millet, cottonsesame, castor-castor, pearl millet-pearl millet and sesame-sesame) with 3 fertilizer treatments (control, integrated nutrient management (INM) and recommended dose of fertilizer for different crops) during 1999 to 2005, a statistical selection is made to identify an efficient crop sequence for attaining maximum sustainable yield in a semi-arid Vertisol at Rajkot, Gujarat state. The results revealed that rainfall in individual month, differences of crop sequences, fertilizer treatments and their interaction were significant for groundnut pod equivalent yield. Based on ranking of crop sequences for mean yield and sustainable yield index, groundnut–sesame was found to be highly efficient.
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Abbas, Rizwan, Ghassan Muslim Hassan, Muna Al-Razgan, Mingwei Zhang, Gehad Abdullah Amran, Ali Ahmed Al Bakhrani, Taha Alfakih, Hussein Al-Sanabani, and Sk Md Mizanur Rahman. "A Serendipity-Oriented Personalized Trip Recommendation Model." Electronics 11, no. 10 (May 23, 2022): 1660. http://dx.doi.org/10.3390/electronics11101660.
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Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip.
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Gao, Ming, Yonghan Luo, and Xiaonan Hu. "Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining." Wireless Communications and Mobile Computing 2022 (August 4, 2022): 1–7. http://dx.doi.org/10.1155/2022/9054149.
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Massive Open Online Course (MOOC) has been criticized for low completion rates, and one of the major reasons is that it fails to offer personalized course recommendations for different users with different demands. To solve this problem, this paper proposes a personalized course recommendation model based on convolutional neural network combined with negative sequence pattern mining. The model first models the course-learning sequence as a negative sequence pattern according to the user’s course registration, degree of completion, and final grades, in which, the negative term means that students should not choose and misoperate the principle of courses. Then, it employs a convolutional neural network structure to extract the internal features of negative sequence patterns for representation learning. Finally, through the convolutional sequence-embedding model, each user is recommended with a course list that includes the user’s maximized needs in recent temporal terms and the courses that are easy to be misselected. Experiment results show that the recommended model achieves higher recommendation performance with lower course dropout rate compared to baselines, which provides a new insight for both online and offline course recommendation.
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Butt, Khushbu Khalid, Guohui Li, Fawad Masood, and Sajid Khan. "A Digital Image Confidentiality Scheme Based on Pseudo-Quantum Chaos and Lucas Sequence." Entropy 22, no. 11 (November 11, 2020): 1276. http://dx.doi.org/10.3390/e22111276.
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Several secure image encryption systems have been researched and formed by chaotic mechanisms in current decades. This work recommends an innovative quantum color image encryption method focused on the Lucas series-based substitution box to enhance the competence of encryption. The suggested encryption technique has more excellent key space and significant confidentiality. The chaotic system, along with the substitution box, exhibits additional complicated dynamical behavior, sufficient arbitrariness, and uncertainty than all others focused on just chaotic models. Theoretical and simulation assessments show that the offered image encryption performs admirably, its traditional equivalents in terms by efficiency in terms of statistical analysis.
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Duthie, Edmund H., Kathryn Denson, Deborah Simpson, Steven Denson, and Amanda Szymkowski. "YES, CHANGING HOW YOU TEACH DOES MAKE A DIFFERENCE." Innovation in Aging 3, Supplement_1 (November 2019): S886. http://dx.doi.org/10.1093/geroni/igz038.3244.
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Abstract Perceptions of an educational experience’s value impact learning. “Hands-on” activities promote deeper learning and retention. Educators may jettison more poorly rated sessions, not having time for perceived content revisions based on evaluation data. We sought to determine if simply changing the sequence of a session’s activities, using the same content, improved learner evaluations. Using a session focused on application of resources for dementia patient caregivers, we provided two versions of the same content to 2 groups of clinicians. In session version #1 (V1), participants were asked about caregiver stresses and barriers and then viewed two video triggers of a dementia patient and a stressed family caregiver. Participants then identified the caregiver’s struggles and recommended resources. At the session’s end they were provided with a Geriatric Fast Fact (GFF) (www.geriatricfastfacts.com) that hyperlinked to a variety of evidence-based resources by topic. In session version #2 (V2), only the content was flipped. The GFF was presented prior to the video, with clinicians were then tasked to identify best resources using the GFF. The V2 cohort rated the session higher than V1 cohort on a 4-point scale (1= Excellent, 4= Poor). Overall quality of learning plan (V1 =1.4 ; V2 =1.3); Would you recommend the session to peers (V1 = 1.5; and V2 =1.2) and Overall course evaluation (V1 = 1.5; V2. = 1.4) all improved. Using learner evaluations to revise the sequence of the same content was an effective educational strategy. Don’t throw the baby out with the bathwater!
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Sulce, Romena, A. Z. Avlokulov, and N. Sh Abdieva. "Improving the Application of Analytical Procedures based on International Auditing Standards." International Journal of Multicultural and Multireligious Understanding 8, no. 10 (October 15, 2021): 271. http://dx.doi.org/10.18415/ijmmu.v8i10.3128.
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In this article discussed the directions of application of analytical procedures based on international auditing standards. It also highlights the importance of analytical procedures in planning the audit, gathering evidence and forming the auditor's report. As a result of the study was recommended the sequence of application of analytical procedures.
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Jones, Alistair C., Jehannine Austin, Nancy Hansen, Bastiaan Hoogendoorn, Peter J. Oefner, Jeremy P. Cheadle, and Michael C. O’Donovan. "Optimal Temperature Selection for Mutation Detection by Denaturing HPLC and Comparison to Single-Stranded Conformation Polymorphism and Heteroduplex Analysis." Clinical Chemistry 45, no. 8 (August 1, 1999): 1133–40. http://dx.doi.org/10.1093/clinchem/45.8.1133.
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Abstract Background: Denaturing HPLC (DHPLC) is a semi-automated method for detecting unknown DNA sequence variants. The sensitivity of the method is dependent on the temperature at which the analysis is undertaken, the selection of which is dependent on operator experience. To circumvent this, software has been developed for predicting the optimal temperature for DHPLC analysis. We examined the utility of this software. Methods: To maximize the relevance of our data for other investigators, we have screened 42 different amplimers from CFTR, TSC1, and TSC2. The samples consisted of 103 unique sequence heterozygotes and 126 wild-type homozygous controls. Results: At the temperature recommended by the software, 96% (99 of 103) of heterozygotes and all of the wild-type controls were correctly classified. This compares favorably with sensitivities of 85% for single-stranded conformation polymorphism and 82% for gel-based heteroduplex analyses of the same fragments. Conclusions: Software-optimized DHPLC is a highly sensitive method for mutation detection. However, where sensitivity >96% is required, our data suggest that in addition to the recommended temperature, fragments should also be run at the recommended temperature plus 2 °C.
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Madhaiyan, Munusamy, Venkatakrishnan Sivaraj Saravanan, Joseph S. Wirth, and William B. Whitman. "Reclassification of Sphingomonas aeria as a later heterotypic synonym of Sphingomonas carotinifaciens based on whole-genome sequence analysis." International Journal of Systematic and Evolutionary Microbiology 70, no. 4 (April 1, 2020): 2355–58. http://dx.doi.org/10.1099/ijsem.0.004045.
Full textAbstract:
The 16S rRNA gene sequences of Sphingomonas carotinifaciens L9-754T and Sphingomonas aeria B093034T possess 99.71 % sequence similarity. Further studies were undertaken to clarify the taxonomic assignments of these species. Whole-genome comparisons showed that S. aeria B093034Tand S. carotinifaciens L9-754T shared 96.9 % average nucleotide identity, 98.4 % average amino acid identity and 76.1 % digital DNA–DNA hybridization values. These values exceeded or approached the recommended species delineation threshold values. Furthermore, a phylogenetic tree based on 41 of the most conserved genes provided additional evidence that S. aeria B093034T and S. carotinifaciens L9-754T are very closely related. Based on this evidence we propose the reclassification of S. aeria Xue et al. 2018 as a later heterotypic synonym of S. carotinifaciens Madhaiyan et al. 2017.
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Guo, Joyce, Lianne Parkin, Jiaxu Zeng, David Barson, and Simon Horsburgh. "Treatment pathways in people with type 2 diabetes mellitus: a nationwide cohort study of new users of metformin monotherapy in New Zealand." BMJ Open 11, no. 8 (August 2021): e051884. http://dx.doi.org/10.1136/bmjopen-2021-051884.
Full textAbstract:
ObjectivesThe aims of this study were to describe the following: (1) the time to change of therapy in patients with type 2 diabetes who had initiated metformin monotherapy as first-line treatment and (2) the sequence in which subsequent therapeutic regimens were introduced.DesignCohort study.SettingNational study based on linked data from the New Zealand Ministry of Health’s National Collections of health and pharmaceutical dispensing data.ParticipantsPeople with type 2 diabetes mellitus who initiated metformin monotherapy between 1 January 2006 and 30 September 2014 (n=93 874).Primary outcome measuresCumulative incidence curves were plotted to show the time taken to move from one regimen to another, while sunburst plots were used to illustrate the sequence in which regimens were introduced.ResultsAbout 10% and 35% of cohort members had moved to a second regimen 1 year and 5 years, respectively, after initiating metformin monotherapy; the majority received a regimen recommended by New Zealand treatment guidelines (mostly metformin and a sulphonylurea). Of those who started a recommended second regimen, 37% and 67% had moved to a third regimen after 1 and 5 years, respectively; the corresponding proportions for those who started an ‘other’ (not listed as recommended) second regimen were 53% and 75%. Most of those who received a third regimen after a recommended second regimen were dispensed an ‘other’ third regimen. Of those who moved to a third regimen from an ‘other’ second regimen, similar proportions received recommended and ‘other’ third regimens.ConclusionsReal-world type 2 diabetes treatment patterns in New Zealand are complex and not always consistent with guidelines.
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Dhindsa, Harkirat S., and David F. Treagust. "Prospective pedagogy for teaching chemical bonding for smart and sustainable learning." Chem. Educ. Res. Pract. 15, no. 4 (2014): 435–46. http://dx.doi.org/10.1039/c4rp00059e.
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As an important subject in the curriculum, many students find chemistry concepts difficult to learn and understand. Chemical bonding especially is important in understanding the compositions of chemical compounds and related concepts and research has shown that students struggle with this concept. In this theoretical paper based on analysis of relevant science education research, textbooks, and our classroom observations and teaching experiences, the authors argue that the difficulty in learning chemical bonding concepts is associated with the sequence (ionic, covalent and polar covalent bonding) in which students are taught because this sequence receives little support from constructivist theories of learning. Consequently, the paper proposes a sequence to teach chemical bonding (covalent, polar covalent and ionic bonding) for effective and sustainable learning. In this sequence, the concepts are developed with minimum reorganisation of previously learned information, using a format which is claimed to be easy for students to learn. For teaching these concepts, the use of electronegativity and the overlap of atomic orbitals for all types of bonding have also been stressed. The proposed sequence and emphasis on electronegativity and atomic orbital overlap meets the criteria for teaching and learning of concepts based on the psychology of learning including the theory of constructivism necessitating the construction of new knowledge using related prior knowledge. It also provides a better linkage between the bonding concepts learned at secondary and tertiary levels. Considering these proposed advantages for teaching, this sequence is recommended for further research into effective and sustainable teaching.
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Han, Jiceng, Yan Gao, Jianbin Cai, Deyuan Lin, Hongjie Zhang, and Yangsen Li. "Analysis on Intensity Matching Coefficient of Power Distribution Circuit Based on System Reliability Theory." E3S Web of Conferences 145 (2020): 02083. http://dx.doi.org/10.1051/e3sconf/202014502083.
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The design intensities of four parts, i.e. ground conductor, hardware fittings, pole tower and foundation are greatly different, so it is difficult to evaluate the reliability of the whole power distribution circuit exactly. In this thesis, the intensity distribution functions of ground conductor, hardware fittings, pole tower and foundation have been calculated respectively, and then based on the system reliability theory, the reasonable failure sequence of power distribution circuit conforming to economic optimization principle is recommended. Under the assumed failure sequence, the calculation about intensity matching coefficient at the target confidence level is implemented, and finally, the intensity matching coefficients of ground conductor, hardwire fittings, pole tower and foundation of power distribution circuit are given.
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Peak, K. Kealy, Kathleen E. Duncan, Vicki A. Luna, Debra S. King, Peter J. McCarthy, and Andrew C. Cannons. "BacillusStrains Most Closely Related toBacillus nealsoniiAre Not Effectively Circumscribed within the Taxonomic Species Definition." International Journal of Microbiology 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/673136.
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Bacillusstrains with >99.7% 16S rRNA gene sequence similarity were characterized with DNA:DNA hybridization, cellular fatty acid (CFA) analysis, and testing of 100 phenotypic traits. When paired with the most closely related type strain, percent DNA:DNA similarities (%S) for sixBacillusstrains were all far below the recommended 70% threshold value for species circumscription withBacillus nealsonii. An apparent genomic group of fourBacillusstrain pairings with 94%–70%Swas contradicted by the failure of the strains to cluster in CFA- and phenotype-based dendrograms as well as by their differentiation with 9–13 species level discriminators such as nitrate reduction, temperature range, and acid production from carbohydrates. The novelBacillusstrains were monophyletic and very closely related based on 16S rRNA gene sequence. Coherent genomic groups were not however supported by similarly organized phenotypic clusters. Therefore, the strains were not effectively circumscribed within the taxonomic species definition.