Distributed Collaborative Filtering Recommendation Model Based on Expand-Vector

Abstract:

The recommendation system based on collaborative filtering is one of the most popular recommendation mechanisms. However, with the continuous expansion of the system, several problems that traditional collaborative filtering recommendation algorithm (CF) faced such as cold startup, accuracy, and scalability are worsen. In order to address these issues, a distributed collaborative filtering recommendation model based on expand-vector (CF-EV) is proposed. Firstly, the eigenvector is expanded reasonably to get the expand-vector based on the expand-vector model, a new extension measure created in this paper. Then, the nearest neighbor user is found and a more accurate recommendation to the target user is given based on the calculation results. In addition, the further optimization makes it applied to the parallel computing framework successfully. Using the MovieLens dataset, the performance of CF-EV is compared with CF from both sides of recommendation precision and the speedup ratio. Through experimental results, CF-EV overcomes the problem of cold startup. Moreover, the accuracy and recall ratio has been doubled. With the increasing numbers of the computing nodes, the distributed implementation has linear speedup.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2188-2191
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2188
• Online since: July 2014
• Authors: Ye Zhu*, Hong Yi Su, Cai Qun Wang, Bo Yan, Hong Zheng
• Keywords: Collaborative Filtering (CF), Distributed Computing, Expand-Vector, MapReduce, Parallel, Recommend Mechanism

References

[1] Resnick P, Iacovou N, et al. GroupLens: an open architecture for collaborative filtering of netnews: ACM conference on Computer supported cooperative work, ACM, pp.175-186(1994).

DOI: 10.1145/192844.192905

[2] E. Brynjolfsson, Y.J. Hu, M.D. Smith: Manage. Sci. Vol. 49 (2003), p.1580–1596.

[3] Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering/Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc, pp.43-52(1998).

[4] Guangwei Zhang, Deyi Li and Peng Li: submitted to Journal of Software (2007). In Chinese.

[5] Anil R, Friedman E. Mahout in Action [M]. New York: Manning Publications Co, pp.1-2(2011).

[6] Dean J, Ghemawat S. MapReduce: Communications of the ACM, pp.107-113(2008).

[7] White T. Hadoop: The Definitive Guide: The Definitive Guide [M].

[8] Information on http: /httpd. apache. org.

[9] Zhang Y, Liu H. DCFRS: A Distributed Collaborative Filtering Recommender System based on Cloud Computing for Mobile Commerce [J]. Appl. Math, pp.1-10(2013).