SonetRank: leveraging social networks to personalize search

Abstract

Earlier works on personalized Web search focused on the click-through graphs, while recent works leverage social annotations, which are often unavailable. On the other hand, many users are members of the social networks and subscribe to social groups. Intuitively, users in the same group may have similar relevance judgments for queries related to these groups. SonetRank utilizes this observation to personalize the Web search results based on the aggregate relevance feedback of the users in similar groups. SonetRank builds and maintains a rich graph-based model, termed Social Aware Search Graph, consisting of groups, users, queries and results click-through information. SonetRank's personalization scheme learns in a principled way to leverage the following three signals, of decreasing strength: the personal document preferences of the user, of the users of her social groups relevant to the query, and of the other users in the network. SonetRank also uses a novel approach to measure the amount of personalization with respect to a user and a query, based on the query-specific richness of the user's social profile. We evaluate SonetRank with users on Amazon Mechanical Turk and show a significant improvement in ranking compared to state-of-the-art techniques.