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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/23407
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| Title: | Social Network Collaborative Filtering |
| Authors: | Zheng, Rong
Provost, Foster
Ghose, Anindya |
| Issue Date: | 14-Sep-2007 |
| Series/Report no.: | CeDER-07-04 |
| Abstract: | This paper reports on a preliminary empirical study comparing methods
for collaborative filtering (CF) using explicit data on consumers’
social networks. To our knowledge it is the first study to carefully
evaluate the potential of explicit, publicly represented social networks
for making product recommendations. Understanding social-network CF is
important because traditional CF over a large consumer base is
tremendously expensive computationally. An often-ignored aspect of CF is
the selection of the set of users from which to make recommendations.
Social theory tells us that social relationships are likely to connect
similar people. If this similarity is in line with the recommendation
task, they may provide a small, dense set of “recommenders”
for CF. We examine a unique dataset from Amazon.com that contains a
social network of consumer-selected friends. We examine two ways to
incorporate social-network information into CF: using the social network
to restrict the set of recommenders selected, and (further) using
proximity in the social network to modify the traditional CF
calculation. The results show that that CF with social-network members
selected as recommenders can be remarkably superior as compared to
collaborative filtering with the recommenders not socially connected.
Once the social network is selected, social network proximity does not
seem to improve recommendations. |
| URI: | http://hdl.handle.net/2451/23407 |
| Appears in Collections: | CeDER Working Papers
IOMS: Information Systems Working Papers
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