Multi-Task Learning for Document Ranking and Query SuggestionDownload PDF

15 Feb 2018 (modified: 10 Feb 2022)ICLR 2018 Conference Blind SubmissionReaders: Everyone
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, a document ranker, and a 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. As both tasks are driven by the users' underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms 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
Code: [![github](/images/github_icon.svg) wasiahmad/mnsrf_ranking_suggestion](
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