Evaluation of the Recommendation Effect of Individual Interventions in a Recommender System

Abstract

Collaborative filtering is a recommendation model that evaluates and recommends items that match the preferences of each user and with high purchase probability. However, some items are regularly purchased and do not necessarily require recommendation. Thus, identifying items with a high recommendation effect is one of the challenges when using recommendation models. The recommendation effect can be regarded as an intervention effect in causal inference by considering the recommendation of an item as intervention. The intervention effect of individual users can be estimated using models such as counterfactual regression (CFR). Nonetheless, because this model can only manage one type of intervention, it cannot be easily and directly applied to recommender systems when many recommended items appear as intervention. In this study, we extend this model to estimate the effects of individual interventions in multiple interventions using a single CFR by combining user and item features to form covariates of user–item pairs, thereby allowing the recommendation effects to be estimated for all user–item pairs. We demonstrate the effectiveness of the proposed method through experiments using artificial data.