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Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning.

, and . CoRR, (2020)

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Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce., , , , , , and . CoRR, (2019)A challenge for rounded evaluation of recommender systems., , , , , , and . Nat. Mac. Intell., 5 (2): 181-182 (February 2023)Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning., and . CoRR, (2020)Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction., , and . NAACL-HLT, page 4409-4415. Association for Computational Linguistics, (2021)Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence., , and . DATA, page 340-347. SciTePress, (2019)Beyond NDCG: behavioral testing of recommender systems with RecList., , , , and . CoRR, (2021)Beyond NDCG: Behavioral Testing of Recommender Systems with RecList., , , , and . WWW (Companion Volume), page 99-104. ACM, (2022)EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments., , , , , , , and . KDD, page 5851-5852. ACM, (2023)Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories., , , , and . ICDM, page 216-220. ibai Publishing, (2019)"Does it come in black?" CLIP-like models are zero-shot recommenders., , , , and . CoRR, (2022)