Journal articles: 'EFFICIENT RECOMMENDER SYSTEMS'

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Ribeiro, Marco Tulio, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, Anisio Lacerda, and Adriano Veloso. "Multiobjective Pareto-Efficient Approaches for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 5, no. 4 (January 23, 2015): 1–20. http://dx.doi.org/10.1145/2629350.

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Huang, Zhen Hua, Dong Wang, and Sheng Li Sun. "Efficient Mining of Skyrank Items in Recommender Systems." Advanced Materials Research 472-475 (February 2012): 3450–54. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3450.

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Mining of skyrank items has recently received a lot of attention in recommender system community. Literature [3] presents an efficient algorithm ZHYX to produce the skyrank items in one single subspace. However, in multi-user environments, recommender systems generally receive multiple subspace skyrank queries simultaneously. Hence, in this paper, we propose the first efficient sound and complete algorithm, i.e. AMMSSI(Algorithm for Mining Multiple Subsapce Skyrank Items), to markedly reduce the total response time. The detailed theoretical analyses and extensive experiments demonstrate that our proposed algorithm is both efficient and effective.

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Hawashin, Bilal, Shadi Alzubi, Tarek Kanan, and Ayman Mansour. "An efficient semantic recommender method forArabic text." Electronic Library 37, no. 2 (April 1, 2019): 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.

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PurposeThis paper aims to propose a new efficient semantic recommender method for Arabic content.Design/methodology/approachThree semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.FindingsExperiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.Originality/valueAlthough many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.

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Pasdar, Amirmohammad, Young Choon Lee, Tahereh Hassanzadeh, and Khaled Almi’ani. "Resource Recommender for Cloud-Edge Engineering." Information 12, no. 6 (May 25, 2021): 224. http://dx.doi.org/10.3390/info12060224.

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The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ).

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Jabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.

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Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

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Radlinski, Filip, Craig Boutilier, Deepak Ramachandran, and Ivan Vendrov. "Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12287–93. http://dx.doi.org/10.1609/aaai.v36i11.21492.

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The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.

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Luo, Chenhong, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang, and Leo Yu Zhang. "An Efficient Approach to Manage Natural Noises in Recommender Systems." Algorithms 16, no. 5 (April 27, 2023): 228. http://dx.doi.org/10.3390/a16050228.

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Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.

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Cui, Zeyu, Feng Yu, Shu Wu, Qiang Liu, and Liang Wang. "Disentangled Item Representation for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (March 2021): 1–20. http://dx.doi.org/10.1145/3445811.

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Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this article, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications.

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Vaidhehi, V., and R. Suchithra. "A Systematic Review of Recommender Systems in Education." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.

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Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.

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Hawashin, Bilal, Darah Aqel, Shadi Alzubi, and Mohammad Elbes. "Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests." Recent Advances in Computer Science and Communications 13, no. 2 (June 3, 2020): 240–47. http://dx.doi.org/10.2174/2213275912666190115162311.

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Background: Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior. Methods: This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings. Results: Experimental work shows that such expanding is efficient in terms of accuracy and execution time. Conclusion: Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.

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Torres, Nicolás, and Marcelo Mendoza. "Clustering Approaches for Top-k Recommender Systems." International Journal on Artificial Intelligence Tools 28, no. 05 (August 2019): 1950019. http://dx.doi.org/10.1142/s0218213019500192.

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Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

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Thanh, Tran Thi. "A STUDY ON MOVIE RECOMMENDER SYSTEMS BASED ON WORDPRESS PLATFORM." International Journal of Engineering Technologies and Management Research 7, no. 6 (July 3, 2020): 152–55. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.709.

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The rapid growth of data collection has led to a new era of information. Data is being used to create more efficient systems and this is where Recommendation Systems come into play. Recommender systems are among the most effcient tools for information filtering to improve the quality of search results and provide items that are more relevant to the search item or are realted to the search history of the user, especially from big data on Internet. Among those, movie recommendation systems are the useful tools to assist users in classifying them with similar interests. This makes them a central part of websites and e-commerce applications. This paper aims to describe the implementation of a movie recommender system built on the Wordpress platform to be able to take advantage of the plugin support system and outstanding management and statistical features. The obtained results indicate that the proposed approach may provide high performance regarding reliability, efficiency, and accuracy. Moreover, the user-friendly interface and suitable display for devices ranging from desktop to mobile devices are also the advantages.

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Ben-Porat, Omer, Lee Cohen, Liu Leqi, Zachary C. Lipton, and Yishay Mansour. "Modeling Attrition in Recommender Systems with Departing Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6072–79. http://dx.doi.org/10.1609/aaai.v36i6.20554.

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Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interaction. However, in real-world systems, dissatisfied users may depart (and never come back). In this work, we propose a novel multi-armed bandit setup that captures such policy-dependent horizons. Our setup consists of a finite set of user types, and multiple arms with Bernoulli payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward probability. Each user's type is initially unknown and can only be inferred through their response to recommendations. Moreover, if a user is dissatisfied with their recommendation, they might depart the system. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types. While naive approaches cannot handle this setting, we provide an efficient learning algorithm that achieves O(sqrt(T)ln(T)) regret, where T is the number of users.

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Lin, Dongding, Jian Wang, and Wenjie Li. "COLA: Improving Conversational Recommender Systems by Collaborative Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4462–70. http://dx.doi.org/10.1609/aaai.v37i4.25567.

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Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representations are crucial for CRS. Despite various attempts, earlier studies mostly learned item representations based on individual conversations, ignoring item popularity embodied among all others. Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose a collaborative augmentation (COLA) method to simultaneously improve both item representation learning and user preference modeling to address these issues. We construct an interactive user-item graph from all conversations, which augments item representations with user-aware information, i.e., item popularity. To improve user preference modeling, we retrieve similar conversations from the training corpus, where the involved items and attributes that reflect the user's potential interests are used to augment the user representation through gate control. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our method. Our code and data are available at https://github.com/DongdingLin/COLA.

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Luo, Xin, Mengchu Zhou, Shuai Li, Yunni Xia, Zhuhong You, Qingsheng Zhu, and Hareton Leung. "An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems." IEEE Transactions on Industrial Informatics 11, no. 4 (August 2015): 946–56. http://dx.doi.org/10.1109/tii.2015.2443723.

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Indira, K., and M. K. Kavithadevi. "Efficient Machine Learning Model for Movie Recommender Systems Using Multi-Cloud Environment." Mobile Networks and Applications 24, no. 6 (October 16, 2019): 1872–82. http://dx.doi.org/10.1007/s11036-019-01387-4.

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Boudaa, Boudjemaa, Djamila Figuir, Slimane Hammoudi, and Sidi mohamed Benslimane. "DATAtourist." International Journal of Decision Support System Technology 13, no. 2 (April 2021): 62–84. http://dx.doi.org/10.4018/ijdsst.2021040104.

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Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.

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Ghosh, Aritra, Saayan Mitra, and Andrew Lan. "DiPS: Differentiable Policy for Sketching in Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6703–12. http://dx.doi.org/10.1609/aaai.v36i6.20625.

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In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i.e., heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies.

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Batra, Priya, Anukriti Singh, and T. S. Mahesh. "Efficient Characterization of Quantum Evolutions via a Recommender System." Quantum 5 (December 6, 2021): 598. http://dx.doi.org/10.22331/q-2021-12-06-598.

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We demonstrate characterizing quantum evolutions via matrix factorization algorithm, a particular type of the recommender system (RS). A system undergoing a quantum evolution can be characterized in several ways. Here we choose (i) quantum correlations quantified by measures such as entropy, negativity, or discord, and (ii) state-fidelity. Using quantum registers with up to 10 qubits, we demonstrate that an RS can efficiently characterize both unitary and nonunitary evolutions. After carrying out a detailed performance analysis of the RS in two qubits, we show that it can be used to distinguish a clean database of quantum correlations from a noisy or a fake one. Moreover, we find that the RS brings about a significant computational advantage for building a large database of quantum discord, for which no simple closed-form expression exists. Also, RS can efficiently characterize systems undergoing nonunitary evolutions in terms of quantum discord reduction as well as state-fidelity. Finally, we utilize RS for the construction of discord phase space in a nonlinear quantum system.

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Sarwat, Mohamed, Justin J. Levandoski, Ahmed Eldawy, and Mohamed F. Mokbel. "LARS*: An Efficient and Scalable Location-Aware Recommender System." IEEE Transactions on Knowledge and Data Engineering 26, no. 6 (June 2014): 1384–99. http://dx.doi.org/10.1109/tkde.2013.29.

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Wang, Zehong, Jianhua Liu, Shigen Shen, and Minglu Li. "Restaurant Recommendation in Vehicle Context Based on Prediction of Traffic Conditions." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 10 (August 2021): 2159044. http://dx.doi.org/10.1142/s0218001421590448.

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Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

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Jeong, Hanjo, and Kyung Jin CHA. "An Efficient MapReduce-Based Parallel Processing Framework for User-Based Collaborative Filtering." Symmetry 11, no. 6 (June 3, 2019): 748. http://dx.doi.org/10.3390/sym11060748.

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User-based collaborative filtering is one of the most-used methods for the recommender systems. However, it takes time to perform the method because it requires a full scan of the entire data to find the neighboring users of each active user, who have similar rating patterns. It also requires time-consuming computations because of the complexity of the algorithms. Furthermore, the amount of rating data in the recommender systems grows rapidly, as the number of users, items, and their rating activities tend to increase. Thus, a big data framework with parallel processing, such as Hadoop, is needed for the recommender systems. There are already many research studies on the MapReduce-based parallel processing method for collaborative filtering. However, most of the research studies have not considered the sequential-access restriction for executing MapReduce jobs and the minimization of the required full scan on the entire data on the Hadoop Distributed File System (HDFS), because HDFS sequentially access data on the disk. In this paper, we introduce an efficient MapReduce-based parallel processing framework for collaborative filtering method that requires only a one-time parallelized full scan, while adhering to the sequential access patterns on Hadoop data nodes. Our proposed framework contains a novel MapReduce framework, including a partial computation framework for calculating the predictions and finding the recommended items for an active user with such a one-way parallelized scan. Lastly, we have used the MovieLens dataset to show the validity of our proposed method, mainly in terms of the efficiency of the parallelized method.

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Starke, Alain, Martijn Willemsen, and Chris Snijders. "Promoting Energy-Efficient Behavior by Depicting Social Norms in a Recommender Interface." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–32. http://dx.doi.org/10.1145/3460005.

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How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, social norms and other nudges are studied to understand how people can be convinced to take action (e.g., towel re-use is boosted when stating that “75% of hotel guests” do so), but most of these nudges are not personalized. In contrast, recommender systems know what to recommend in a personalized way, but not much human-computer interaction ( HCI ) research has considered how personalized advice should be presented to help users to change their current habits. We examine the value of depicting normative messages (e.g., “75% of users do X”), based on actual user data, in a personalized energy recommender interface called “Saving Aid.” In a study among 207 smart thermostat owners, we compared three different normative explanations (“Global.” “Similar,” and “Experienced” norm rates) to a non-social baseline (“kWh savings”). Although none of the norms increased the total number of chosen measures directly, we show that depicting high peer adoption rates alongside energy-saving measures increased the likelihood that they would be chosen from a list of recommendations. In addition, we show that depicting social norms positively affects a user’s evaluation of a recommender interface.

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Do, Virginie, Sam Corbett-Davies, Jamal Atif, and Nicolas Usunier. "Online Certification of Preference-Based Fairness for Personalized Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6532–40. http://dx.doi.org/10.1609/aaai.v36i6.20606.

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Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity assessments at the level of sensitive groups. We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users. Since auditing for envy requires to estimate the preferences of users beyond their existing recommendations, we cast the audit as a new pure exploration problem in multi-armed bandits. We propose a sample-efficient algorithm with theoretical guarantees that it does not deteriorate user experience. We also study the trade-offs achieved on real-world recommendation datasets.

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Bouni, Mohamed, Badr Hssina, Khadija Douzi, and Samira Douzi. "Towards an Efficient Recommender Systems in Smart Agriculture: A deep reinforcement learning approach." Procedia Computer Science 203 (2022): 825–30. http://dx.doi.org/10.1016/j.procs.2022.07.124.

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Wang, Pengyu, Yong Wang, Leo Yu Zhang, and Hong Zhu. "An effective and efficient fuzzy approach for managing natural noise in recommender systems." Information Sciences 570 (September 2021): 623–37. http://dx.doi.org/10.1016/j.ins.2021.05.002.

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Drif, Ahlem, and Hocine Cherifi. "MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering." Entropy 24, no. 8 (August 5, 2022): 1084. http://dx.doi.org/10.3390/e24081084.

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Many web platforms now include recommender systems. Network representation learning has been a successful approach for building these efficient recommender systems. However, learning the mutual influence of nodes in the network is challenging. Indeed, it carries collaborative signals accounting for complex user-item interactions on user decisions. For this purpose, in this paper, we develop a Mutual Interaction Graph Attention Network “MIGAN”, a new algorithm based on self-supervised representation learning on a large-scale bipartite graph (BGNN). Experimental investigation with real-world data demonstrates that MIGAN compares favorably with the baselines in terms of prediction accuracy and recommendation efficiency.

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Liu, Lewis, and Kun Zhao. "Asynchronous Stochastic Gradient Descent for Extreme-Scale Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 328–35. http://dx.doi.org/10.1609/aaai.v35i1.16108.

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Recommender systems are influential for many internet applications. As the size of the dataset provided for a recommendation model grows rapidly, how to utilize such amount of data effectively matters a lot. For a typical Click-Through-Rate(CTR) prediction model, the amount of daily samples can probably be up to hundreds of terabytes, which reaches dozens of petabytes at an extreme-scale when we take several days into consideration. Such data makes it essential to train the model parallelly and continuously. Traditional asynchronous stochastic gradient descent (ASGD) and its variants are proved efficient but often suffer from stale gradients. Hence, the model convergence tends to be worse as more workers are used. Moreover, the existing adaptive optimizers, which are friendly to sparse data, stagger in long-term training due to the significant imbalance between new and accumulated gradients. To address the challenges posed by extreme-scale data, we propose: 1) Staleness normalization and data normalization to eliminate the turbulence of stale gradients when training asynchronously in hundreds and thousands of workers; 2) SWAP, a novel framework for adaptive optimizers to balance the new and historical gradients by taking sampling period into consideration. We implement these approaches in TensorFlow and apply them to CTR tasks in real-world e- commerce scenarios. Experiments show that the number of workers in asynchronous training can be extended to 3000 with guaranteed convergence, and the final AUC is improved by more than 5 percentage.

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Chicaiza, Janneth, and Priscila Valdiviezo-Diaz. "A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions." Information 12, no. 6 (May 28, 2021): 232. http://dx.doi.org/10.3390/info12060232.

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In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

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Stitini, O., S. Kaloun, and O. Bencharef. "INVESTIGATING DIFFERENT SIMILARITY METRICS USED IN VARIOUS RECOMMENDER SYSTEMS TYPES: SCENARIO CASES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 187–93. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-187-2022.

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Abstract. A recommendation system represents a very efficient way to propose solutions adapted to customers needs. It allows users to discover interesting items from a large amount of data according to their preferences. To do this, it uses a similarity metric, which determines how similar two users or products are. In the case of recommender systems, similarity computation is a practical step. The calculation of similarity may be used for both items and users. Following the similarity calculation, a user or item with a comparable computation value can be recommended together with the goods to a user with similar preferences. The user’s requirements influence the choice of similarity metric. This paper explores various similarity measurement methods employed in recommender systems. We compare correlation and distance techniques to determine the capabilities of different similitude calculation algorithms and synthesize which similarity measure is adapted for which type of recommendation.

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Yan, Surong, Kwei-Jay Lin, Xiaolin Zheng, Wenyu Zhang, and Xiaoqing Feng. "An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks." IEEE Transactions on Knowledge and Data Engineering 29, no. 10 (October 1, 2017): 2086–99. http://dx.doi.org/10.1109/tkde.2017.2717984.

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Xin Luo, Mengchu Zhou, Yunni Xia, and Qingsheng Zhu. "An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems." IEEE Transactions on Industrial Informatics 10, no. 2 (May 2014): 1273–84. http://dx.doi.org/10.1109/tii.2014.2308433.

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Ibrahim, Muhammad, and Imran Bajwa. "Design and Application of a Multi-Variant Expert System Using Apache Hadoop Framework." Sustainability 10, no. 11 (November 19, 2018): 4280. http://dx.doi.org/10.3390/su10114280.

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Movie recommender expert systems are valuable tools to provide recommendation services to users. However, the existing movie recommenders are technically lacking in two areas: first, the available movie recommender systems give general recommendations; secondly, existing recommender systems use either quantitative (likes, ratings, etc.) or qualitative data (polarity score, sentiment score, etc.) for achieving the movie recommendations. A novel approach is presented in this paper that not only provides topic-based (fiction, comedy, horror, etc.) movie recommendation but also uses both quantitative and qualitative data to achieve a true and relevant recommendation of a movie relevant to a topic. The used approach relies on SentiwordNet and tf-idf similarity measures to calculate the polarity score from user reviews, which represent the qualitative aspect of likeness of a movie. Similarly, three quantitative variables (such as likes, ratings, and votes) are used to get final a recommendation score. A fuzzy logic module decides the recommendation category based on this final recommendation score. The proposed approach uses a big data technology, “Hadoop” to handle data diversity and heterogeneity in an efficient manner. An Android application collaborates with a web-bot to use recommendation services and show topic-based recommendation to users.

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Maazouzi, Faiz, Hafed Zarzour, and Yaser Jararweh. "An Effective Recommender System Based on Clustering Technique for TED Talks." International Journal of Information Technology and Web Engineering 15, no. 1 (January 2020): 35–51. http://dx.doi.org/10.4018/ijitwe.2020010103.

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With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

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Liu, Hanwen, Huaizhen Kou, Chao Yan, and Lianyong Qi. "Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph." Complexity 2020 (April 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/2085638.

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Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.

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narayan, subhashini. "Multilayer Perceptron with Auto encoder enabled Deep Learning model for Recommender Systems." Future Computing and Informatics Journal 5, no. 2 (December 30, 2020): 96–116. http://dx.doi.org/10.54623/fue.fcij.5.2.3.

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In this modern world of ever-increasing one-click purchases, movie bookings, music, healthcare, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. This research article propose a recommendation system based on deep learning models such as multilayer perceptron that would provide a slightly more efficient and accurate recommendations.

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Liu, Hai, Chao Zheng, Duantengchuan Li, Xiaoxuan Shen, Ke Lin, Jiazhang Wang, Zhen Zhang, Zhaoli Zhang, and Neal N. Xiong. "EDMF: Efficient Deep Matrix Factorization With Review Feature Learning for Industrial Recommender System." IEEE Transactions on Industrial Informatics 18, no. 7 (July 2022): 4361–71. http://dx.doi.org/10.1109/tii.2021.3128240.

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Chen, Jiawei, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, and Xiangnan He. "CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation." ACM Transactions on Information Systems 39, no. 3 (May 22, 2021): 1–24. http://dx.doi.org/10.1145/3450289.

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Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instances with uniform distribution, which, however, will severely affect a model’s convergence, stability, and even recommendation accuracy. A promising solution for this problem is to over-sample the “difficult” (a.k.a. informative) instances that contribute more on training. But this will increase the risk of biasing the model and leading to non-optimal results. Moreover, existing samplers are either heuristic, which require domain knowledge and often fail to capture real “difficult” instances, or rely on a sampler model that suffers from low efficiency. To deal with these problems, we propose CoSam, an efficient and effective collaborative sampling method that consists of (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency, and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling. Correspondingly, we derive a fast reinforced training algorithm of our framework to boost the sampler performance and sampler-recommender collaboration. Extensive experiments on four real-world datasets demonstrate the superiority of the proposed collaborative sampler model and integrated sampler-recommender framework.

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Nadimi-Shahraki, Mohammad-Hossein, and Mozhde Bahadorpour. "Cold-start Problem in Collaborative Recommender Systems: Efficient Methods Based on Ask-to-rate Technique." Journal of Computing and Information Technology 22, no. 2 (2014): 105. http://dx.doi.org/10.2498/cit.1002223.

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Wei, Lingtao. "Communication Efficient Federated Personalized Recommendation." Frontiers in Computing and Intelligent Systems 2, no. 3 (February 13, 2023): 63–67. http://dx.doi.org/10.54097/fcis.v2i3.5214.

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Recommendation systems that can correctly predict user preferences in the information age have become an important factor for business success. However, recommendation systems require users' personal information, and centralized collection and processing of user data may lead to serious privacy risks. Good progress has been made in recent years using federated learning techniques for privacy-preserving recommendations, but several key challenges remain to be addressed: most federated recommender systems ignore communication process optimization, inequities in aggregation of federated models, and lack of personalization to users. In this paper, we propose a communication efficient and fair personalized federated recommendation approach (CFFR) to address these challenges. CFFR uses adaptive client group selection to personalize models while accelerating the training process. A fair-aware model aggregation algorithm is proposed that adaptively captures the performance and data imbalance among different clients to address the unfairness problem. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.

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Rrmoku, Korab, Besnik Selimi, and Lule Ahmedi. "Provenance and social network analysis for recommender systems: a literature review." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5383. http://dx.doi.org/10.11591/ijece.v12i5.pp5383-5392.

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<span>Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network.</span>

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Cho, Gyungah, Pyoung-seop Shim, and Jaekwang Kim. "Explainable B2B Recommender System for Potential Customer Prediction Using KGAT." Electronics 12, no. 17 (August 22, 2023): 3536. http://dx.doi.org/10.3390/electronics12173536.

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The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between B2B and business-to-consumer (B2C), the B2B recommender system should be defined differently. This paper presents a new perspective on the explainable B2B recommender system using the knowledge graph attention network for recommendation (KGAT). Unlike traditional recommendation systems that suggest products to consumers, this study focuses on recommending potential buyers to sellers. Additionally, the utilization of the KGAT attention mechanisms enables the provision of explanations for each company’s recommendations. The Korea Electronic Taxation System Association provides the Market Transaction Dataset in South Korea, and this research shows how the dataset is utilized in the knowledge graph (KG). The main tasks can be summarized in three points: (i) suggesting the application of an explainable recommender system in B2B for recommending potential customers, (ii) extracting the performance-enhancing features of a knowledge graph, and (iii) enhancing keyword extraction for trading items to improve recommendation performance. We can anticipate providing good insight into the development of the industry via the utilization of the B2B recommendation of potential customer prediction.

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Khan, Zeshan Aslam, Naveed Ishtiaq Chaudhary, Waqar Ali Abbasi, Sai Ho Ling, and Muhammad Asif Zahoor Raja. "Design of Confidence-Integrated Denoising Auto-Encoder for Personalized Top-N Recommender Systems." Mathematics 11, no. 3 (February 2, 2023): 761. http://dx.doi.org/10.3390/math11030761.

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A recommender system not only “gains users’ confidence” but also helps them in other ways, such as reducing their time spent and effort. To gain users’ confidence, one of the main goals of recommender systems in an e-commerce industry is to estimate the users’ interest by tracking the users’ transactional behavior to provide a fast and highly related set of top recommendations out of thousands of products. The standard ranking-based models, i.e., the denoising auto-encoder (DAE) and collaborative denoising auto-encoder (CDAE), exploit positive-only feedback without utilizing the ratings’ ranks for the full set of observed ratings. To confirm the rank of observed ratings (either low or high), a confidence value for each rating is required. Hence, an improved, confidence-integrated DAE is proposed to enhance the performance of the standard DAE for solving recommender systems problems. The correctness of the proposed method is authenticated using two standard MovieLens datasets such as ML-1M and ML-100K. The proposed study acts as a vital contribution for the design of an efficient, robust, and accurate algorithm by learning prominent latent features used for fast and accurate recommendations. The proposed model outperforms the state-of-the-art methods by achieving improved P@10, R@10, NDCG@10, and MAP scores.

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Ivanova, M. I. "Recommender systems in the public administration: methodological overview and conceptualization." Journal of Law and Administration 17, no. 2 (July 16, 2021): 61–69. http://dx.doi.org/10.24833/2073-8420-2021-2-59-61-69.

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Introduction. The article discusses approaches to describing recommender systems in public administration in the context of digital transformation of smart cities. Recommender systems are information filtering and recommendation mechanisms designed to facilitate and increase the speed of decision making. The effectiveness of public administration depends on the ability of state bodies not only to promptly respond to emerging challenges, but also on the ability to foresee such situations, to develop possible scenarios for future developments based on a retrospective analysis of available data, which will become possible due to the implementation of recommendation systems in the general canvas of the state digital platforms. Despite the lack of unambiguity in understanding the concept of a smart city, the scientific community emphasizes the importance of technological infrastructures not only for the life of the urban area, but also for the process of making management decisions. The scientific corps crystallizes the idea of a smart city as a functional urban area created by means of information and communication technologies, without which it becomes impossible to manage the city in an efficient and sustainable way. Over the past 20 years, the original concept of a smart city, conceived as a way to achieve more sustainable urban development, has gradually evolved to address the problems of ineffective governance. In this context, striving to improve such aspects as the quality of life of citizens, as well as the empowerment of their rights and opportunities, the smart city becomes a kind of environment in which the citizen is the center of all services and initiatives taking place in a given territory, where the use of technology plays the most important role.Materials and methods. The methodological basis of the research is characterized by the following general scientific methods: analysis, synthesis, systemic and functional approaches.Discussion and conclusion. As a result of the study, it was revealed that recommender systems should become part of the decision-making process in the field of public administration. The question of the quality of the recommendations provided remains unresolved, since the effectiveness of the recommendation systems depends on factors that go beyond the quality of the forecasting algorithm.

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Agarwal, Vipul, and Vijayalakshmi A. "Recommender system for surplus stock clearance." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3813. http://dx.doi.org/10.11591/ijece.v9i5.pp3813-3821.

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Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

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Wang, Xixian, Xiaoming Wang, Binrui Huang, Mingzhan Dai, and Jianwei Li. "Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation." Security and Communication Networks 2022 (September 16, 2022): 1–15. http://dx.doi.org/10.1155/2022/8607234.

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With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.

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Le, Quang-Hung, Son-Lam Vu, Thi-Kim-Phuong Nguyen, and Thi-Xinh Le. "A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications." International Journal of Knowledge and Systems Science 12, no. 3 (July 2021): 1–20. http://dx.doi.org/10.4018/ijkss.2021070101.

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In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

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Et. al., Geluvaraj B,. "AMatrix factorization technique using parameter tuning of singular value decomposition for Recommender Systems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 3313–19. http://dx.doi.org/10.17762/turcomat.v12i2.2390.

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In this article we explained the concepts of SVD and algorithm evolution. MF technique and the working of it with computational formulas. PCA withstep-by-step approach with example and A novel approach of Hyper SVD and How to fine tune it and pseudocode of the Hyper SVD with the Experimental setup using SurpriseLib and computing RMSE and MSE for the accuracy purpose and solving with the real time example which solves the cold start hassle also together and it can be seen that comparison of SVD and Hyper SVD and Random algorithm is done and types of Movies they recommended. There is far more difference between the results of the both algorithms and movie recommendations as per the results Hyper SVD is flexible and efficient and superior compared to other algorithms.

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Nittu, Goutham, Singh Karan, Banda Latha, Sharma Purushottam, Verma Chaman, and Goyal S. B. "ShAD-SEF: An Efficient Model for Shilling Attack Detection using Stacking Ensemble Framework in Recommender Systems." International Journal of Performability Engineering 19, no. 5 (2023): 291. http://dx.doi.org/10.23940/ijpe.23.05.p1.291302.

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Hu, Kerui, Lemiao Qiu, Shuyou Zhang, Zili Wang, Naiyu Fang, and Huifang Zhou. "A novel neighbor selection scheme based on dynamic evaluation towards recommender systems." Science Progress 106, no. 2 (April 2023): 003685042311800. http://dx.doi.org/10.1177/00368504231180090.

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Collaborative filtering is a kind of widely used and efficient technique in various online environments, which generates recommendations based on the rating information of his/her similar-preference neighbors. However, existing collaborative filtering methods have some inadequacies in revealing the dynamic user preference change and evaluating the recommendation effectiveness. The sparsity of input data may further exacerbate this issue. Thus, this paper proposes a novel neighbor selection scheme constructed in the context of information attenuation to bridge these gaps. Firstly, the concept of the preference decay period is given to describe the pattern of user preference evolution and recommendation invalidation, and thus two types of dynamic decay factors are correspondingly defined to gradually weaken the impact of old data. Then, three dynamic evaluation modules are built to evaluate the user's trustworthiness and recommendation ability. Finally, A hybrid selection strategy combines these modules to construct two neighbor selection layers and adjust the neighbor key thresholds. Through this strategy, our scheme can more effectively select capable and trustworthy neighbors to provide recommendations. The experiments on three real datasets with different data sizes and data sparsity show that the proposed scheme provides excellent recommendation performance and is more suitable for real applications, compared to the state-of-the-art methods.

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Journal articles on the topic 'EFFICIENT RECOMMENDER SYSTEMS'

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