Personalized web search by using learned user profiles in re-ranking
Search engines return results mainly based on the submitted query; however, the same query could be in different contexts because individual users have different interests. To improve the relevance of search results, we propose re-ranking results based on a learned user profile. In our previous work we introduced a scoring function for re-ranking search results based on a learned User Interest Hierarchy (UIH). Our results indicate that we can improve relevance at lower ranks, but not at the top 5 ranks. In this thesis, we improve the scoring function by incorporating new term characteristics, image characteristics and pivoted length normalization. Our experimental evaluation shows that the proposed scoring function can improve relevance in each of the top 10 ranks.