Paper: Statistical Machine Translation Models for Personalized Search

ACL ID I08-1068
Title Statistical Machine Translation Models for Personalized Search
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008

Web search personalization has been well studied in the recent few years. Relevance feedback has been used in various ways to improve relevance of search results. In this paper, we propose a novel usage of rele- vance feedback to effectively model the pro- cess of query formulation and better char- acterize how a user relates his query to the document that he intends to retrieve using a noisy channel model. We model a user profile as the probabilities of translation of query to document in this noisy channel us- ing the relevance feedback obtained from the user. The user profile thus learnt is applied in a re-ranking phase to rescore the search results retrieved using an underlying search engine. We evaluate our approach by con- ducting experiments using relevance feed- back data collected...