Web Search Personalisation Using Social Data

Abstract

Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional Information Retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics have only demonstrated limited improvements. This paper proposes an Iterative Personalized Query Expansion Algorithm for Web Search (iPAW), which is based on individual user profiles mined from the annotations and resources the user has marked. The method also incorporates a user model constructed from a cooccurrence matrix and from a Tag-Topic model where annotations and web documents are connected in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. An ?adaptivity factor? was further investigated to adjust the level of personalization.