Social Network Collaborative Filtering

23 Pages Posted: 19 Nov 2008

See all articles by Rong Zheng

Rong Zheng

Hong Kong University of Science and Technology - Business School - Department of Information Systems, Business Statistics and Operations Management

Dennis Wilkinson

affiliation not provided to SSRN

Foster Provost

New York University

Date Written: October 2008

Abstract

This paper demonstrates that "social network collaborative filtering" (SNCF), wherein user-selected like-minded alters are used to make predictions, can rival traditional user-to-user collaborative filtering (CF) in predictive accuracy. Us-ing a unique data set from an online community where users rated items and also created social networking links specifically intended to represent like-minded â¬Sallies,â¬? we use SNCF and traditional CF to predict ratings by net-worked users. We find that SNCF using generic "friend" alters is moderately worse than the better CF techniques, but outperforms benchmarks such as by-item or by-user average rating; generic friends often are not like-minded. However, SNCF using "ally" alters is competitive with CF. These results are significant because SNCF is tremendously more computationally efficient than traditional user-user CF and may be implemented in large-scale web commerce and social networking communities. It is notoriously difficult to distinguish the contributions of social influence (where allies influence users) and "socialâ¬? selection (where users are simply effective at selecting like-minded people as their allies). Nonetheless, comparing similarity over time, we do show no evi-dence of strong social influence among allies or friends.

Suggested Citation

Zheng, Rong and Wilkinson, Dennis and Provost, Foster, Social Network Collaborative Filtering (October 2008). Stern, IOMS Department, CeDER, Vol. , pp. -, 2008. Available at SSRN: https://ssrn.com/abstract=1303924

Rong Zheng (Contact Author)

Hong Kong University of Science and Technology - Business School - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Dennis Wilkinson

affiliation not provided to SSRN

No Address Available

Foster Provost

New York University ( email )

44 West Fourth Street
New York, NY 10012
United States

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