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Добірка наукової літератури з теми "Sequence-aware recommender system"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Sequence-aware recommender system"

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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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Анотація:
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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Sun, Ninghua, Tao Chen, Longya Ran, and Wenshan Guo. "Dynamic and Static Features-Aware Recommendation with Graph Neural Networks." Computational Intelligence and Neuroscience 2022 (April 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/5484119.

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Анотація:
Recommender systems are designed to deal with structured and unstructured information and help the user effectively retrieve needed information from the vast number of web pages. Dynamic information of users has been proven useful for learning representations in the recommender system. In this paper, we construct a series of dynamic subgraphs that include the user and item interaction pairs and the temporal information. Then, the dynamic features and the long- and short-term information of users are integrated into the static recommendation model. The proposed model is called dynamic and static features-aware graph recommendation, which can model unstructured graph information and structured tabular data. Particularly, two elaborately designed modules are available: dynamic preference learning and dynamic sequence learning modules. The former uses all user-item interactions and the last dynamic subgraph to model the dynamic interaction preference of the user. The latter captures the dynamic features of users and items by tracking the preference changes of users over time. Extensive experiments on two publicly available datasets show that the proposed model outperforms several compelling state-of-the-art baselines.
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3

Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. "Sequence-Aware Recommender Systems." ACM Computing Surveys 51, no. 4 (September 6, 2018): 1–36. http://dx.doi.org/10.1145/3190616.

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4

Xu, Yanan, Yanmin Zhu, and Jiadi Yu. "Modeling Multiple Coexisting Category-Level Intentions for Next Item Recommendation." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–24. http://dx.doi.org/10.1145/3441642.

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Анотація:
Purchase intentions have a great impact on future purchases and thus can be exploited for making recommendations. However, purchase intentions are typically complex and may change from time to time. Through empirical study with two e-commerce datasets, we observe that behaviors of multiple types can indicate user intentions and a user may have multiple coexisting category-level intentions that evolve over time. In this article, we propose a novel Intention-Aware Recommender System (IARS) which consists of four components for mining such complex intentions from user behaviors of multiple types. In the first component, we utilize several Recurrent Neural Networks (RNNs) and an attention layer to model diverse user intentions simultaneously and design two kinds of Multi-behavior GRU (MGRU) cells to deal with heterogeneous behaviors. To reveal user intentions, we carefully design three tasks that share representations from MGRUs. The next-item recommendation is the main task and leverages attention to select user intentions according to candidate items. The remaining two (item prediction and sequence comparison) are auxiliary tasks and can reveal user intentions. Extensive experiments on the two real-world datasets demonstrate the effectiveness of our models compared with several state-of-the-art recommendation methods in terms of hit ratio and NDCG.
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5

Mahmud, Umar. "UML based Model of a Context Aware System." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 1 (January 2015): 1–16. http://dx.doi.org/10.4018/ijapuc.2015010101.

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Анотація:
Context Awareness is the mechanism through which systems can adapt to the needs of a user by monitoring the context. Context includes environment, spatial, temporal, etc information that is used to infer the current activity. UML is used to design a context aware system. The context aware system is viewed as an Object Oriented software product. The UML model is generated through ArgoUML, a free UML modelling tool. The Use Case Diagram, the Sequence Diagrams and the Class Diagram are modelled using this tool. The Class Diagram is subjected to CK metrics to identify the strengths and weaknesses of the design. The measurements show that the proposed model is within the recommended range.
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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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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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Анотація:
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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8

Levitan, Michael M., Gary E. Crawford, and Andrew Hardwick. "Practical Considerations for Pressure-Rate Deconvolution of Well Test Data." SPE Journal 11, no. 01 (March 1, 2006): 35–47. http://dx.doi.org/10.2118/90680-pa.

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Summary Pressure-rate deconvolution provides equivalent representation of variable-rate well-test data in the form of characteristic constant rate drawdown system response. Deconvolution allows one to develop additional insights into pressure transient behavior and extract more information from well-test data than is possible by using conventional analysis methods. In some cases, it is possible to interpret the same test data in terms of larger radius of investigation. There are a number of specific issues of which one has to be aware when using pressure-rate deconvolution. In this paper, we identify and discuss these issues and provide practical considerations and recommendations on how to produce correct deconvolution results. We also demonstrate reliable use of deconvolution on a number of real test examples. Introduction Evaluation and assessment of pressure transient behavior in well-test data normally begins with examination of test data on different analysis plots [e.g., a Bourdet (1983, 1989) derivative plot, a superposition (semilog) plot, or a Cartesian plot]. Each of these plots provides a different view of the pressure transient behavior hidden in the test data by well-rate variation during a test. Integration of these several views into one consistent picture allows one to recognize, understand, and explain the main features of the test transient pressure behavior. Recently, a new method of analyzing test data in the form of constant rate drawdown system response has emerged with development of robust pressure-rate deconvolution algorithm. (von Schroeter et al. 2001, 2004; Levitan 2005). Deconvolved drawdown system response is another way of presenting well-test data. Pressure--rate deconvolution removes the effects of rate variation from the pressure data measured during a well-test sequence and reveals underlying characteristic system behavior that is controlled by reservoir and well properties and is not masked by the specific rate history during the test. In contrast to a Bourdet derivative plot or to a superposition plot, which display the pressure behavior for a specific flow period of a test sequence, deconvolved drawdown response is a representation of transient pressure behavior for a group of flow periods included in deconvolution. As a result, deconvolved system response is defined on a longer time interval and reveals the features of transient behavior that otherwise would not be observed with conventional analysis approach. The deconvolution discussed in this paper is based on the algorithm first described by von Schroeter, Hollaender, and Gringarten (2001, 2004). An independent evaluation of the von Schroeter et al. algorithm by Levitan (2005) confirmed that with some enhancements and safeguards it can be used successfully for analysis of real well-test data. There are several enhancements that distinguish our form of the deconvolution algorithm. The original von Schroeter algorithm reconstructs only the logarithm of log-derivative of the pressure response to constant rate production. Initial reservoir pressure is supposed to be determined in the deconvolution process along with the deconvolved drawdown system response. However, inclusion of the initial pressure in the list of deconvolution parameters often causes the algorithm to fail. For this reason, the authors do not recommend determination of initial pressure in the deconvolution process (von Schroeter et al. 2004). It becomes an input parameter and has to be evaluated through other means. Our form of deconvolution algorithm reconstructs the pressure response to constant rate production along with its log-derivative. Depending on the test sequence, in some cases we can recover the initial reservoir pressure.
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9

Kala, K. U., and M. Nandhini. "Context-Category Specific sequence aware Point-Of-Interest Recommender System with Multi-Gated Recurrent Unit." Journal of Ambient Intelligence and Humanized Computing, December 9, 2019. http://dx.doi.org/10.1007/s12652-019-01583-w.

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10

Liu, Xiao, Bonan Gao, Basem Suleiman, Han You, Zisu Ma, Yu Liu, and Ali Anaissi. "Privacy-Preserving Personalized Fitness Recommender System ( P 3 FitRec ) : A Multi-level Deep Learning Approach." ACM Transactions on Knowledge Discovery from Data, January 13, 2023. http://dx.doi.org/10.1145/3572899.

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ABSTRACT Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users’ privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this paper, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable IoT devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data and thus minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, weight. In particular, our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared to similar studies. 1 Furthermore, our approach is novel compared to existing studies as it does not require collecting and using users’ sensitive information, and thus it preserves the users’ privacy.
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Книги з теми "Sequence-aware recommender system"

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1st, Kala K. U., and Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.

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Частини книг з теми "Sequence-aware recommender system"

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Kala, K. U., and M. Nandhini. "Two-Way Sequence Modeling for Context-Aware Recommender Systems with Multiple Interactive Bidirectional Gated Recurrent Unit." In Lecture Notes in Electrical Engineering, 129–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2612-1_12.

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Zhou, Mingming, and Yabo Xu. "Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners." In Educational Recommender Systems and Technologies, 282–301. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-489-5.ch012.

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Анотація:
A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.
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Zhou, Mingming, and Yabo Xu. "Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners." In Data Mining, 1916–35. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch099.

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Анотація:
A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.
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Тези доповідей конференцій з теми "Sequence-aware recommender system"

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Quadrana, Massimo, Paolo Cremonesi, and Dietmar Jannach. "Sequence-aware Recommender Systems." In UMAP '18: 26th Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209219.3209270.

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Quadrana, Massimo, and Paolo Cremonesi. "Sequence-aware recommendation." In RecSys '18: Twelfth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240323.3241621.

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Quadrana, Massimo, Dietmar Jannach, and Paolo Cremonesi. "Tutorial: Sequence-Aware Recommender Systems." In WWW '19: The Web Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3308560.3320091.

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Felicioni, Nicolò, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu, Cesare Bernardis, and Maurizio Ferrari Dacrema. "Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction." In RecSys Challenge '20: Proceedings of the Recommender Systems Challenge 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3415959.3415998.

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