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A Tensor Decomposition Based Approach for Context-Aware Recommender Systems (CARS)

Published: 04 November 2021 Publication History

Abstract

Recommender Systemsare used to suggest items of interest to users so that their overall browsing experience of the website is enhanced as well as they are not overwhelmed with the abundance of available information. The benefit of incorporating context in recommender systems is evident as the preferences of the users are highly dependent of the context in which they are making the decision.In our proposed approach, we have used context as an explicit feature to improve the recommendations so that it can adapt to the user's needs according to different scenario.We have extended the traditional two dimensional matrix factorization used in collaborative filtering to N-dimensional tensor factorization. Tensor appropriately models the different ratings given by a user to the same item in different scenario. The experimental results obtained using contextual variables proved to be of higher accuracy.

References

[1]
Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.
[2]
Adomavicius, G., &Tuzhilin, A. (2011). Context-aware recommender systems. In Recommender systems handbook (pp. 217-253). Springer, Boston, MA.
[3]
Dey, A. K. (2001). Understanding and using context. Personal and ubiquitous computing, 5(1), 4-7.
[4]
Zheng, Y., Mobasher, B., & Burke, R. (2015, November). Carskit: A java-based context-aware recommendation engine. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 1668-1671). IEEE.
[5]
Meehan, K., Lunney, T., Curran, K., & McCaughey, A. (2013, March). Context-aware intelligent recommendation system for tourism. In 2013 IEEE international conference on pervasive computing and communications workshops (PERCOM workshops) (pp. 328-331). IEEE.
[6]
Yujie, Z., &Licai, W. (2010, August). Some challenges for context-aware recommender systems. In 2010 5th International Conference on Computer Science & Education (pp. 362-365). IEEE.
[7]
Kuleshov, V., Chaganty, A., & Liang, P. (2015, February). Tensor factorization via matrix factorization. In Artificial Intelligence and Statistics (pp. 507-516). PMLR.
[8]
Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010, September). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 79-86).
[9]
Sheugh, L., &Alizadeh, S. H. (2018). A novel 2D-Graph clustering method based on trust and similarity measures to enhance accuracy and coverage in recommender systems. Information Sciences, 432, 210-230.
[10]
Vairachilai, S., Urkude, S., &Hemalatha, J. (2018). A survey on recommendation system: Collaborative filtering. J. Adv. Res. Dyn. Control Syst., 1, 1850-1857.
[11]
Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3), 455-500.

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  • (2022)Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender SystemsIEEE Access10.1109/ACCESS.2022.319242710(75581-75595)Online publication date: 2022

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cover image ACM Other conferences
IC3-2021: Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing
August 2021
483 pages
ISBN:9781450389204
DOI:10.1145/3474124
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2021

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Author Tags

  1. Collaborative filtering
  2. Context Aware Recommendation
  3. Recommender Systems
  4. Tensor Factorization

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Cited By

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  • (2022)Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender SystemsIEEE Access10.1109/ACCESS.2022.319242710(75581-75595)Online publication date: 2022

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