Abstract
Context-aware Recommender Systems (CARS) deal with modeling and prediction of user interests and preferences according to contextual information while generating a recommendation. In contextual modeling-based CARS, the context information is used straight into the recommendation function as a predictor explicitly. Thus, this approach formulates a multidimensional recommendation model and is best realized through Tensor Factorization (TF) based techniques. It efficiently handles the data sparsity problem faced by most of the traditional RS. However, the recent TF-based CARS face issues such as differentiating amongst relevant and irrelevant context variables, biased recommendations, and long-tail problem. In this paper, we propose a fusion-based approach for determining the list of most relevant and optimum contexts for two datasets, namely the LDos Comoda and Travel dataset. The mutual trust model that combines user level and item level trust is proposed further which utilizes the concept of trust propagation to calculate the inferred trust between users/items. Finally, a hybrid reranking technique combining the item popularity and item absolute likeability reranking approaches with the standard ranking technique of generating recommendations is proposed to generate diversified recommendations. Comparative experiments on the LDos Comoda and the Travel datasets are conducted and the experimental results show an improvement of the proposed work with respect to RMSE of 50%, 55%, and 59% compared to MF-based RS, trust-based RS, and context-aware RS respectively. Also, the proposed reranking technique shows approximately three times more diversified recommendations than the standard ranking approach without a significant loss in precision.
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References
Adomavicius G, Sankaranarayanan R, Sen S and Tuzhilin A 2005 Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1) pp. 103–145
Adomavicius G, Mobasher B, Francesco R, and Tuzhilin A 2011 Context-aware recommender systems. In: Recommender Systems Handbook, Springer, pp. 217–253
Verbert K, Manouselis N, Ochoa X, Wolpers M, Drachsler H, Bosnic I and Duval E 2012 Context-aware recommender systems for learning: a survey and future challenges. IEEE transactions on Learning Technologies, 5(4) pp. 318–335
Panniello U and Gorgoglione M 2012 Incorporating context into recommender systems: an empirical comparison of context-based approaches. Electronic Commerce Research, 12(1) pp. 1–30
Karatzoglou A, Amatriain X, Baltrunas L and Oliver N 2010 Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the fourth ACM Conference on Recommender Systems, pp. 79–86
Rabanser S, Shchur O and Günnemann S 2017 Introduction to tensor decompositions and their applications in machine learning. arXiv:1711.10781
Odić A, Tkalčič M, Tasič Jurij F and Košir A 2013 Predicting and detecting the relevant contextual information in a movie-recommender system. Interacting with Computers, 25(1) pp. 74–90
Golbeck J 2006 Generating predictive movie recommendations from trust in social networks. In: Proceedings of the Springer International Conference on Trust Management, pp. 93–104
Massa P and Avesani P 2007 Trust-aware recommender systems. In: Proceedings of the ACM International Conference on Recommender Systems, pp. 17–24
Hwang C and Chen Y 2007 Using trust in collaborative filtering recommendation. In: Proceedings of the Springer International Conference on Industrial Engineering and other Applications of Applied Intelligent Systems, pp. 1052–1060
Shambour Q and Lu J 2015 An effective recommender system by unifying user and item trust information for B2B applications. Journal of Computer and System Sciences, 81(1) pp. 1110–1126
Sejwal V and Abulaish M 2019 Trust and context-based rating prediction using collaborative filtering: a hybrid approach. In: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics pp. 1–10
Adomavicius G and Kwon Y 2011 Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering, 24(5) pp. 896–911
Adomavicius G and Kwon Y 2011 Maximizing aggregate recommendation diversity: A graph-theoretic approach. In: Proceedings of the 1st Citeseer International Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011) pp. 3–10
Liu H, Bai X, Yang Z, Tolba A and Xia F 2015 Trust-aware recommendation for improving aggregate diversity. New Review of Hypermedia and Multimedia, 21(3-4) pp.242–258
Kunaver M and Požrl T 2017 Diversity in recommender systems–A survey. Knowledge-based systems, 123 pp.154–162
Campos P, Fernández-Tobías I, Cantador I and Díez F 2013 Context-aware movie recommendations: an empirical comparison of pre-filtering, post-filtering and contextual modeling approaches. In: Proceedings of the Springer International Conference on Electronic Commerce and Web Technologies, pp. 137–149
Liu L, Lecue F, Mehandjiev N and Xu L 2010 Using context similarity for service recommendation. In: Proceedings of the 4th IEEE International Conference on Semantic Computing, pp. 277–284
Baltrunas L, Ludwig B, Peer S and Ricci F 2012 Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing, 16(5) pp. 507–526
Li J, Yang R and Jiang L 2016 DTCMF: Dynamic trust-based context-aware matrix factorization for collaborative filtering. In: Proceedings of the IEEE International Conference on Information Technology, Networking, Electronic and Automation Control, pp. 914–919
Odić A, Tkalčič M, Tasič Jurij F and Košir A 2012 Relevant context in a movie recommender system: Users’ opinion vs. statistical detection. In: ACM RecSys’12
Selmi A, Brahmi Z and Gammoudi Mohamed M 2016 Trust-based recommender systems: an overview. In: Proceedings of 27th International Business Information Management Association (IBIMA) Conference, Milan, Italy
Massa P and Avesani P 2004 Trust-aware collaborative filtering for recommender systems. In: OTM Confederated Springer International Conferences on the Move to Meaningful Internet Systems, pp. 492–508
O’Donovan J and Smyth B 2005 Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 167–174
Moradi P and Ahmadian S 2015 A reliability-based recommendation method to improve trust-aware recommender systems. Expert Systems with Applications, 42(21) pp. 7386–7398
Jamali M and Ester M 2010 A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 135–142
Adomavicius G and Kwon Y 2009 Toward more diverse recommendations: Item re-ranking methods for recommender systems. In: Citeseer Workshop on Information Technologies and Systems
Papagelis M, Plexousakis D and Kutsuras T 2005 Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Proceedings of the Springer International Conference on Trust Management, pp. 224–239
Li W, Zhou X, Shimizu S, Xin M, Jiang J, Gao H and Jin Q 2019 Personalization recommendation algorithm based on trust correlation degree and matrix factorization. IEEE Access, 7 pp. 45451–45459
Koren Y, Bell R and Volinsky C 2009 Matrix factorization techniques for recommender systems. IEEE Computer, 42(8) pp. 30–37
Lathia N, Hailes S and Capra L 2008 Trust-based collaborative filtering. In: Proceedings of the IFIP Springer International Conference on Trust Management, pp. 119–134
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Patil, V., Jayaswal, D. Enhancing the aggregate diversity with mutual trust computations for context-aware recommendations. Sādhanā 47, 29 (2022). https://doi.org/10.1007/s12046-021-01795-x
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DOI: https://doi.org/10.1007/s12046-021-01795-x