This paper demonstrates that quot;social network collaborative filteringquot; (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 acirc;not;Sallies,acirc;not;? we use SNCF and traditional CF to predict ratings by net-worked users. We find that SNCF using generic quot;friendquot; 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 quot;allyquot; 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 quot;socialacirc;not;? 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
