Information search and discovery engines now rely on not just personalized models of interests, but also the social cues created by a large number of people. The attention traces left behind by people are valuable navigational signposts for building social recommenders. We can take advantage of the fact that these traces are being generated in a social context, with networks of friends and friends-of-friends as potential audiences and transceivers. In this talk, I will talk about the use of these cues in two systems:

First, in MrTaggy.com, we used the social cues from social bookmarks sites. Social tagging arose out of the need to organize found information that is worth revisiting. The collective behavior of users who tagged contents offer a good basis for recommendation engines. We used information theory and probabilistic graph models to pre-compute recommendations, and evaluated this exploratory browsing system in the lab using end-user learning metrics.

Second, in Zerozero88.com, we constructed a tweet recommender for Twitter users. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We evaluated the system by having twitter users rank the recommendations we gave them over a 3 week period. The results show how recommenders can profitably integrate social cues.

(joint work with Jilin Chen and Rowan Nairn)