Presentation information

General Session

General Session » GS-2 Machine learning

[3G2-GS-2h] 機械学習:推論

Thu. Jun 10, 2021 11:00 AM - 12:40 PM Room G (GS room 2)

座長:黒木 隆之(VOYAGE GROUP)

12:20 PM - 12:40 PM

[3G2-GS-2h-05] A Study on Item Recommendation Model by Evaluating the Effect of Individual Intervention

〇Taichi Imafuku1, Tatuya Kawakami1, Tianxiang Yang1, Masayuki Goto1 (1. Waseda University)

Keywords:Recommender System, Causal Inference, Individual Treatment Effect, Neural Network

Collaborative filtering is one of the item recommendation models which recommends items with high estimated purchase probability to each user. However, among the items with high estimated purchase probability, there are some items that are purchased regularly and thus have little need of recommendation. Therefore, one of the challenges for recommender systems is to identify items with high recommendation effect.
CounterFactual Regression (CFR) is known as a model to estimate the effect of individual intervention such as recommendation effect. However, since this model can only handle one type of intervention, it is difficult to apply it to a recommendation system where there are many recommended items as interventions.
In this study, we extend the model to estimate recommendation effect with a single CFR by combining user and item features to form covariates of user-item pairs. Finally, we demonstrate the effectiveness of the proposed method through experiments using artificial data.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.