38 citations · 148 across the 20 of their papers we have counts for
5 papers · 1 filter
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Yuta Saito, Masahiro Nomura
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IP…
Dual Learning Algorithm for Delayed Conversions
Yuta Saito, Gota Morishita, Shota Yasui
In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an advertiser's website, is a fundamental task for es…
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
Yuta Saito
In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the l…
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito, Shota Yasui
We study the model selection problem in conditional average treatment effect (CATE) prediction. Unlike previous works on this topic, we focus on preserving the rank order of the pe…
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Yuta Saito, Suguru Yaginuma, Yuta Nishino +2
Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some exten…