An Overview of Collaborative Filtering Recommendation Algorithm

Abstract:

Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3899-3903
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3899
• Online since: September 2013
• Authors: Ping Sun, Zheng Yu Li, Zi Yang Han, Feng Ying Wang
• Keywords: Augmentability, Collaborative Filtering (CF), Recommendation Algorithm, Sparseness

References

[1] Rich E. User modeling via stereotypes. Cognitive Science, 1979, 3(4): 329-354.

[2] Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACE, 1992, 35(12): 61-70.

DOI: 10.1145/138859.138867

[3] Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J. GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM, 1997, 40(3): 77-87.

DOI: 10.1145/245108.245126

[4] Shardanand U, Maes P. Social information filtering: Algorithms for automating"Word of Mouth". In: Proc. Of the Conf. on Human Factors in Computing Systems. New York: ACM Press, 1995. 210-217.

DOI: 10.1145/223904.223931

[5] Goldberg K, Roeder T, Gupta D, Perkins C. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 2001, 4(2): 133-151.

[6] Breese J S, Heckerman D, Kadie C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering [C] / / Process of the 14th Conference on Uncertainty in Artificial Intelligence, 1998: 43-52.

[7] Resnick P, Iakovou N, Sushak M , et al. GroupLens: An open Architecture for Collaborative Filtering of Netnews[C]/ Process of the 1994 Computer Supported Cooperative Work Conference, 1994: 175-186.

DOI: 10.1145/192844.192905

[8] Sarwar B, Karypis G, Konstan J. Item-based Collaborative Filtering Recommendation Algorithms[ C] / / Process of the 10th International Conference on World Wide Web, 2001: 285-295.

DOI: 10.1145/371920.372071

[9] Chen Y H, George E I. A Bayesian Model for Collaborative Filtering [C] / / Process of the 7th International Workshop on Artificial Intelligence and Statistics, (1999).

[10] Billsus D, Pazzani M. Learning Collaborative Information Filters[C] / / Process of International Conferenceon Machine Learning, 1998: 46-54.

[11] Kim B M, Li Q, Park C S, et al . A new approach for combining content-based and collaborative filters[J]. Journal of Intelligent Inf ormation Systems, 2006, 27(1) : 79-91.

DOI: 10.1007/s10844-006-8771-2

[12] Vozalis M G, Margaritis K G. Applying SVD on item-based filtering [C] . In: Proceedings of 5th International Conference on Intelligent Systems Design and Applications (ISDA'05) 2005, 464- 469.

DOI: 10.1109/isda.2005.25

[13] Zhang Hai-yan, Ding Feng, Jiang Li-hong. A collaborative filtering recommendation method based on fuzzy clustering[ J] . Computer Simulation, 2005, 22( 8) : 144- 147.

[14] B M Sarwar, G Karypis, J Konstan, et al. Item-Based Collaborative Filtering Recommendation Algorithms, Process of the 10th Internet Conference on World Wide Web[C]: New York: ACMpress, 2001: 285-295.

DOI: 10.1145/371920.372071