A Novel Approach for Re-Ranking of Search Results Using Collaborative Filtering

Search engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and context. Re-ranking of the results to reflect the most relevant results to the user using the relevance feedback has received wide attention in information retrieval in recent years. Also, sharing of information among users having similar interests using collaborative filtering techniques has achieved wide success in recommendation systems. In this paper, we propose a novel approach for re-ranking of the search results using collaborative filtering techniques using relevance feedback of a given user as well as the other users. Our approach is to learn the profiles of the users using machine learning techniques making use of past browsing histories including queries posed and documents found relevant or irrelevant. Re-ranking of the results is done using collaborative filtering techniques. First, the context of the query is inferred from the query category. The user's community is determined dynamically in the context of the query by using the user profiles. The rank of a document is calculated using the user's profile as well profiles of the other users in the community