Off-policy evaluation (OPE) estimates the performance of new recommendation policies using logged data, thus enabling fast, safe and inexpensive iteration prior to costly A/B tests. To evaluate ranking policies, existing OPE estimators all make structural assumptions about user behavior, leading to a spectrum of trade-...
Norman Knyazev, Vito Bellini, H. Yurtseven et al.· Proceedings of the 20th ACM...· 0 citations
Industrial recommender systems typically operate in two stages: retrieving a candidate set from a large catalog, then ranking those candidates using contextual information. The ranking stage relies on features that summarize a user’s prior interactions with the system. These features are often carefully hand-crafted, a...
Simon Rauch, Vito Bellini, Anton Thielmann et al.· Proceedings of the 20th ACM...· 0 citations
Off-policy evaluation (OPE) estimates the performance of new recommendation policies using logged data, thus enabling fast, safe and inexpensive iteration prior to costly A/B tests. To evaluate ranking policies, existing OPE estimators all make structural assumptions about user behavior, leading to a spectrum of trade-...
Norman Knyazev, Vito Bellini, H. Yurtseven et al.· Proceedings of the 20th ACM...· 0 citations
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