|
Archive@NYU >
Stern School of Business >
CeDER Working Papers >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27735
|
| Title: | Social Network Collaborative Filtering |
| Authors: | Zheng, Rong
Wilkinson, Dennis
Provost, Foster |
| Issue Date: | 22-Oct-2008 |
| Series/Report no.: | CeDER-08-08 |
| 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 “allies,” 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 "social” 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. |
| URI: | http://hdl.handle.net/2451/27735 |
| Appears in Collections: | CeDER Working Papers
|
All items in Faculty Digital Archive are protected by copyright, with all rights reserved.
|
