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"Does it come in black?" CLIP-like models are zero-shot recommenders., , , , and . CoRR, (2022)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)Beyond NDCG: behavioral testing of recommender systems with RecList., , , , and . CoRR, (2021)You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack.. RecSys, page 598-600. ACM, (2021)eCom'23: The SIGIR 2023 Workshop on eCommerce., , , , , and . SIGIR, page 3476-3478. ACM, (2023)(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis., and . eCom@SIGIR, volume 3589 of CEUR Workshop Proceedings, CEUR-WS.org, (2023)Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce., , , , , , and . CoRR, (2019)Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning., and . CoRR, (2020)