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Contextualized Recommendation Model Based Socio-Environmental Factors

Published: 26 May 2023 Publication History

Abstract

Recommender Systems (RS) often face challenges with new user and data sparsity problems. To address these issues, this paper proposes a new REcommendation Model based sOcio-enVironmEntal factors called "RE-MOVE", which takes into account socio-environmental contextual information. By reviewing state-of-the-art recommendation algorithms, the study found that combining both social and environmental data can improve recommendation quality. The paper argues that modifying the vector of characteristics of items and users can significantly enhance the model. According to the authors, no previous research has investigated matrix factorization by modeling item vector characteristics and considering social and environmental data. The proposed approach was evaluated by comparing it to methods that incorporate contextual information. The results, based on Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), demonstrate that RE-MOVE can more effectively address the challenges and improve the quality of recommendations.

References

[1]
M. F. Ijaz, W. Tao, J. Rhee, Y.-S. Kang, and G. Alfian, “Efficient Digital Signage-Based Online Store Layout: An Experimental Study,” Sustainability, vol. 8, p. 511, May 2016.
[2]
S. Roy, M. Biswas, and D. De, “iMusic: a session-sensitive clustered classical music recommender system using contextual representation learning,” Multimed. Tools Appl., vol. 79, no. 33–34, pp. 24119–24155, Sep. 2020.
[3]
“Health Recommender Systems: Systematic Review - .” https:// .ncbi.nlm.nih.gov/34185014/.
[4]
G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans Knowl Data Eng, vol. 17, no. 6, pp. 734–749, Jun. 2005.
[5]
F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds., Recommender Systems Handbook. Springer US, 2011. Accessed: Apr. 16, 2019. [Online]. Available: https://www.springer.com/gp/book/9780387858203
[6]
J. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” ArXiv13017363 Cs, Jan. 2013, Accessed: May 04, 2019. [Online]. Available: http://arxiv.org/abs/1301.7363
[7]
M. Jallouli, S. Lajmi, and I. Amous, “Latent Factor Model Applied to Recommender System: Realization, Steps and Algorithm,” in Information Systems, M. Themistocleous and V. Morabito, Eds., in Lecture Notes in Business Information Processing. Springer International Publishing, 2017, pp. 606–618.
[8]
Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009,.
[9]
Y. Koren, “Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, in KDD ’08. New York, NY, USA: ACM, 2008, pp. 426–434.
[10]
H. Ma, I. King, and M. R. Lyu, “Learning to Recommend with Social Trust Ensemble,” in Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval, in SIGIR ’09. New York, NY, USA: ACM, 2009, pp. 203–210.
[11]
J. Delporte, A. Karatzoglou, T. Matuszczyk, and S. Canu, “Socially Enabled Preference Learning from Implicit Feedback Data,” Sep. 2013.
[12]
M. Jamali and M. Ester, “A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks,” in Proceedings of the Fourth ACM Conference on Recommender Systems, in RecSys ’10. New York, NY, USA: ACM, 2010, pp. 135–142.
[13]
G. Guo, J. Zhang, and N. Yorke-Smith, “TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings,” in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, in AAAI’15. AAAI Press, 2015, pp. 123–129. Accessed: May 06, 2019.
[14]
L. Eldén, “Numerical linear algebra in data mining,” Acta Numer., vol. 15, pp. 327–384, May 2006.
[15]
A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, “Multiverse Recommendation: N-dimensional Tensor Factorization for context-aware Collaborative Filtering,” presented at the RecSys’10 - Proceedings of the 4th ACM Conference on Recommender Systems, Jan. 2010, pp. 79–86.
[16]
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver, “TFMAP: optimizing MAP for top-n context-aware recommendation,” in Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’12, Portland, Oregon, USA: ACM Press, 2012, p. 155.
[17]
M. Jallouli, S. Lajmi, and I. Amous, “A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System,” Int. J. Strateg. Inf. Technol. Appl., vol. 9, pp. 38–51, Oct. 2018,.
[18]
G. Guo, J. Zhang, D. Thalmann, and N. Yorke-Smith, “ETAF: An extended trust antecedents framework for trust prediction,” in 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Aug. 2014, pp. 540–547.
[19]
M. Jallouli, S. Lajmi, and I. Amous, “Similarity and Trust Metrics Used in Recommender Systems: A Survey,” in Intelligent Systems Design and Applications, A. M. Madureira, A. Abraham, D. Gamboa, and P. Novais, Eds., in Advances in Intelligent Systems and Computing. Springer International Publishing, 2017, pp. 1041–1050.
[20]
C. Ono, Y. Takishima, Y. Motomura, and H. Asoh, “Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Data,” in User Modeling, Adaptation, and Personalization, G.-J. Houben, G. McCalla, F. Pianesi, and M. Zancanaro, Eds., in Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009, pp. 102–113.
[21]
G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach,” ACM Trans Inf Syst, vol. 23, no. 1, pp. 103–145, Jan. 2005.
[22]
“MovieLens,” GroupLens, Sep. 06, 2013. https://grouplens.org/datasets/movielens/ (accessed Apr. 03, 2022).
[23]
M. Jallouli, S. Lajmi, and I. Amous, “Designing Recommender System: Conceptual Framework and Practical Implementation,” Procedia Comput. Sci., vol. 112, pp. 1701–1710, Jan. 2017.
[24]
C. Zheng, H. E, M. Song, and J. Song, “CMPTF: Contextual Modeling Probabilistic Tensor Factorization for recommender systems,” Neurocomputing, vol. 205, pp. 141–151, Sep. 2016,
[25]
L. Baltrunas, B. Ludwig, and F. Ricci, “Matrix Factorization Techniques for Context Aware Recommendation,” in Proceedings of the Fifth ACM Conference on Recommender Systems, in RecSys ’11. New York, NY, USA: ACM, 2011, pp. 301–304.
[26]
M. Jallouli, S. Lajmi, and I. Amous, “When contextual information meets recommender systems: Extended SVD++ models,” Int. J. Comput. Appl. TJCA.
[27]
Y. Koren, “Factor in the Neighbors: Scalable and Accurate Collaborative Filtering,” TKDD, vol. 4, Jan. 2010.

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cover image ACM Other conferences
IDEAS '23: Proceedings of the 27th International Database Engineered Applications Symposium
May 2023
222 pages
ISBN:9798400707445
DOI:10.1145/3589462
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 the author(s) 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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Published: 26 May 2023

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