High Quality Related Search Query Suggestions using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2108.04452v1
• Date: Tue, 10 Aug 2021 05:22:32 GMT
• Title: High Quality Related Search Query Suggestions using Deep Reinforcement Learning
• Authors: Praveen Kumar Bodigutla
• Abstract summary: "High Quality Related Search Query Suggestions" task aims at recommending search queries which are real, accurate, diverse, relevant and engaging. We train a Deep Reinforcement Learning model to predict the query a user would enter next. The reward signal is composed of long-term session-based user feedback, syntactic relatedness and estimated naturalness of generated query.
• Score: 0.15229257192293202
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: "High Quality Related Search Query Suggestions" task aims at recommending search queries which are real, accurate, diverse, relevant and engaging. Obtaining large amounts of query-quality human annotations is expensive. Prior work on supervised query suggestion models suffered from selection and exposure bias, and relied on sparse and noisy immediate user-feedback (e.g., clicks), leading to low quality suggestions. Reinforcement Learning techniques employed to reformulate a query using terms from search results, have limited scalability to large-scale industry applications. To recommend high quality related search queries, we train a Deep Reinforcement Learning model to predict the query a user would enter next. The reward signal is composed of long-term session-based user feedback, syntactic relatedness and estimated naturalness of generated query. Over the baseline supervised model, our proposed approach achieves a significant relative improvement in terms of recommendation diversity (3%), down-stream user-engagement (4.2%) and per-sentence word repetitions (82%).

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