Abstract
Recommender Systems (RSs) have emerged as powerful tools to provide the users with personalized recommendations and to guide them in their decision making process. Among the various recommendation approaches, Collaborative Filtering (CF) is considered as one of the most popular techniques in RSs. CF techniques are categorized into model-based and memory-based. Model-based approaches consist in learning a model from past ratings to perform predictions while memory-based ones predict ratings by selecting the most similar users (user-based) or the most similar items (item-based). In both types, recommendations are fully based on users’ past ratings. However, aside from users’ ratings, exploiting additional information such as items’ features would enhance the accuracy of the provided predictions. Another crucial challenge in the RSs area would be to handle uncertainty arising throughout the prediction process. That is why, in this paper, we propose an item-based Collaborative Filtering under the belief function theory that not only takes advantages of both model- and memory- based CF approaches but also integrates items’ contents in the recommendation process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_1
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence, pp. 1–19. Hindawi Publishing Corporation (2009)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295. ACM (2001)
Jung, S.Y., Hong, J.-H., Kim, T.-S.: A statistical model for user preference. IEEE Trans. Knowl. Data Eng. 17(6), 834–843 (2005)
Dempster, A.P.: A generalization of Bayesian inference. J. Roy. Stat. Soc.: Ser. B (Methodol.) 30, 205–247 (1968)
Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)
Cuzzolin, F.: On the orthogonal projection of a belief function. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 356–367. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75256-1_33
Denoeux, T.: A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25, 804–813 (1995)
Masson, M.H., Denoeux, T.: ECM: an evidential version of the fuzzy c-means algorithm. Pattern Recogn. 41, 1384–1397 (2008)
Zhang, J., Lin, Y., Lin, M., Liu, J.: An effective collaborative filtering algorithm based on user preference clustering. Appl. Intell. 45, 230–240 (2016)
Bellogin, A., Parapar, J.: Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In: ACM Conference on Recommender Systems, pp. 213–216. ACM (2012)
Hwang, T.G., Park, C.S., Hong, J.H., Kim, S.K.: An algorithm for movie classification and recommendation using genre correlation. Multimedia Tools Appl. 75(20), 12843–12858 (2016)
Barragns-Martnez, A.B., Costa-Montenegro, E., Burguillo, J.C., Rey-Lpez, M., Mikic-Fonte, F.A., Peleteiro, A.: A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf. Sci. 180(22), 4290–4311 (2010)
Abdelkhalek, R., Boukhris, I., Elouedi, Z.: Evidential item-based collaborative filtering. In: Lehner, F., Fteimi, N. (eds.) KSEM 2016. LNCS (LNAI), vol. 9983, pp. 628–639. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47650-6_49
Abdelkhalek, R., Boukhris, I., Elouedi, Z.: Assessing items reliability for collaborative filtering within the belief function framework. In: Jallouli, R., Zaïane, O.R., Bach Tobji, M.A., Srarfi Tabbane, R., Nijholt, A. (eds.) ICDEc 2017. LNBIP, vol. 290, pp. 208–217. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62737-3_18
Abdelkhalek, R., Boukhris, I., Elouedi, Z.: A clustering approach for collaborative filtering under the belief function framework. In: Antonucci, A., Cholvy, L., Papini, O. (eds.) ECSQARU 2017. LNCS (LNAI), vol. 10369, pp. 169–178. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61581-3_16
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
Su, X., Khoshgoftaar, T.M.: Collaborative filtering for multi-class data using Bayesian networks. Int. J. Artif. Intell. Tools 17, 71–85 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Abdelkhalek, R., Boukhris, I., Elouedi, Z. (2018). An Evidential Collaborative Filtering Approach Based on Items Contents Clustering. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-99383-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99382-9
Online ISBN: 978-3-319-99383-6
eBook Packages: Computer ScienceComputer Science (R0)