Abstract

Long-term search history contains rich information about a user's search preferences. In this paper, we study statistical language modeling based methods to mine contextual information from longterm search history and to exploit it for more accurate estimates of the query model. The experiments on a web search test collection show that the algorithms are effective in improving retrieval accuracy for both fresh and recurring queries. The best performance is achieved when using the combination of related past searches and clickthrough data as the main source of search context.

Citations