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2017 | OriginalPaper | Chapter

Collaborative Filtering Based on Pairwise User-Item Blocking Structure (PBCF): A General Framework and Its Implementation

Authors : Fengjuan Zhang, Jianjun Wu, Jianzhao Qin, Xing Liu, Yongqiang Wang

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

To our knowledge, all existing collaborative filtering techniques need to find neighbouring relationship between users or items by using some kind of similarity measurement in the feature space. However, a hypothesis hidden behind most existing works is that the similar relationship between users remains static over the whole item sets, which is not true in reality. Users who share similar opinions on some items may have totally different opinions on other items. Users can form many clusters in terms of their opinions on a set of items, However, these clusters may collapse and a new cluster structure will be built in terms their opinions on the new item sets. Analogously, clusters of items formed based on their popularity among a group of users would be disintegrated when encounter a new group of users. In a nutshell, user cluster structure varies across item sets, and vice versa, item cluster structure also varies across user sets.

To deal with this collapse problem, we strive to find block structures embedded in the rating matrix in this paper. Block structure is used to characterize the interaction between users and items. This paper proposes a general framework of collaborative filtering based on pairwise user-item blocking structure and its implementation. At last, existing collaborative filtering algorithms are used to learn the latent factor at the global and block level and further make prediction on the unknown rating in the rating matrix. Experiment evidences show that the recommendation performance can be improved with utilization of these block structures.

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Banerjee, A., Dhillon, I., Ghosh, J., Merugu, S., Modha, D.S.: A generalized maximum entropy approach to bregman co-clustering and matrix approximation. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 509–514. ACM (2004)

Cacheda, F., Carneiro, V., Fernández, D., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web (TWEB) 5(1), 2 (2011)

Chen, G., Wang, F., Zhang, C.: Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Inf. Process. Manag. 45(3), 368–379 (2009)CrossRef

Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269–274. ACM (2001)

Funk, S.: Netflix update: try this at home. http://sifter.org/simon/journal/20061211.html (2006)

George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Fifth IEEE International Conference on Data Mining, p. 4. IEEE (2005)

Hagen, L., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 11(9), 1074–1085 (1992)CrossRef

Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)

Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)CrossRef

10.

Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)

11.

Money, J.H., Ye, Q.: Algorithm 845: EIGIFP: a MATLAB program for solving large symmetric generalized eigenvalue problems. ACM Trans. Math. Softw. (TOMS) 31(2), 270–279 (2005)MathSciNetCrossRefMATH

12.

Ngo, T.T., Bellalij, M., Saad, Y.: The trace ratio optimization problem. SIAM Rev. 54(3), 545–569 (2012)MathSciNetCrossRefMATH

13.

Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD cup and workshop, vol. 2007, pp. 5–8 (2007)

14.

Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)

15.

Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRef

16.

Wang, H., Yan, S., Xu, D., Tang, X., Huang, T.: Trace ratio vs. ratio trace for dimensionality reduction. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)

17.

Wang, J., De Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508. ACM (2006)

18.

Yan, S., Xu, D., Zhang, B., Zhang, H.J.: Graph embedding: A general framework for dimensionality reduction. In: IEEE 2005 Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 830–837. IEEE (2005)

Title: Collaborative Filtering Based on Pairwise User-Item Blocking Structure (PBCF): A General Framework and Its Implementation
Authors: Fengjuan Zhang
Jianjun Wu
Jianzhao Qin
Xing Liu
Yongqiang Wang
Publisher: Springer International Publishing
Book: Knowledge Science, Engineering and Management
Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-63558-3_29

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