Supervised Local Contexts Aggregation for Effective Session Search

Abstract

Existing research on web search has mainly focused on the optimization and evaluation of single queries. However, in some complex search tasks, users usually need to interact with the search engine multiple times before their needs can be satisfied, the process of which is known as session search. The key to this problem relies on how to utilize the session context from preceding interactions to improve the search accuracy for the current query. Unfortunately, existing research on this topic only formulated limited modeling for session contexts, which in fact can exhibit considerable variations. In this paper, we propose Supervised Local Context Aggregation (SLCA) as a principled framework for complex session context modeling. In SLCA, the global session context is formulated as the combination of local contexts between consecutive interactions. These local contexts are further weighted by multiple weighting hypotheses. Finally, a supervised ranking aggregation is adopted for effective optimization. Extensive experiments on TREC11/12 session track show that our proposed SLCA algorithm outperforms many other session search methods, and achieves the state-of-the-art results.