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To support complex search tasks, where the initial information requirementsare complex or may change during the search, a search engine must adapt theinformation delivery as the user's information requirements evolve. To supportthis dynamic ranking paradigm effectively, search result ranking mustincorporate both the user feedback received, and the information displayed sofar. To address this problem, we introduce a novel reinforcement learning-basedapproach, RLIrank. We first build an adapted reinforcement learning frameworkto integrate the key components of the dynamic search. Then, we implement a newLearning to Rank (LTR) model for each iteration of the dynamic search, using arecurrent Long Short Term Memory neural network (LSTM), which estimates thegain for each next result, learning from each previously ranked document. Toincorporate the user's feedback, we develop a word-embedding variation of theclassic Rocchio Algorithm, to help guide the ranking towards the high-valuedocuments. Those innovations enable RLIrank to outperform the previouslyreported methods from the TREC Dynamic Domain Tracks 2017 and exceed all themethods in 2016 TREC Dynamic Domain after multiple search iterations, advancingthe state of the art for dynamic search.

RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search