RecSys 2010: music

How do teenagers learn about new music?  Audrey LaPlante has spent some time actually asking them, and presented some of their answers at the Workshop on Music Recommendation and Discovery.

Some of her findings were extremely interesting.  All of the teenagers she spoke to said that their tastes had changed substantially over time, and that the changes were due to changes in their social network.  Most had music geek friends whom they actively consulted for music recommendations, even though they were not influential people in other respects.  Although close contacts were most likely to be sources of new music, those chosen to play that role were "almost always those whose social network were more different from theirs, mostly those who were going to a different school".

I'll be interested to follow Audrey's research and see how we can learn to make online social networks an equally great place for young people to discover new music.

RecSys 2010: social recommendations

Mohsen Jamali won the best paper award at RecSys for his presentation on SocialMF, a model-based recommender designed to improve ratings-based recommendations for users who have made few ratings but who have friends, or friends of friends, who have provided plenty.  Mohsen's previous solution to the same problem was TrustWalker.  TrustWalker predicts ratings from a set of item-item similarities and a social graph, where nodes are users and edges represent trust or friendship relationships.  The rating for item i for user u is predicted by taking a short random walk on the graph, stopping at some friend-of-a-friend v and returning v's rating for item j, where j is the most similar item to i which v has rated.  Closed form expressions for these predictions don't scale at all well, so to make a prediction TrustWalker actually executes the random walk a few times and returns the average rating.  On a test set of ratings by 50k users for 100k product reviews from the Epinions website, TrustWalker does indeed show a significant benefit in both coverage and prediction accuracy for cold start users over baseline methods that don't leverage the social graph.

SocialMF is a Bayesian model-based solution to the same problem: latent factors for all users and items are learned jointly from ratings and the social graph.  Ratings for cold start users can then be predicted from their learned factors.  When tested on the epinions dataset, and a new one of ratings by 1M users for 50k movies crawled from Flixster, SocialMF again does indeed improve the accuracy of predicted ratings for cold start users over latent factor models that don't take the social graph into account.

The model-based approach is elegant and perhaps even scalable: learning time is linear in the number of users, and the paper reports a runtime of 5.5 hours for 1M users.  But it lacks the powerful explanations of the simpler system: "recommended for you because your friend xyz likes it".