Multi-Task Learning for Document Ranking and Query Suggestion

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, document ranker and query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommender tracks users' query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. Both components are trained across search sessions by sharing parameters through session recurrence, which encodes session information. Comprehensive experiments including rigorous comparisons with state-of-the-art techniques are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.
  • Keywords: Multitask Learning, Document Ranking, Query Suggestion

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