Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure

International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 1508–1518

Abstract

In sequential recommendation, system exposure refers to items that are exposed to the user. Typically, the user only interactions with a few of the exposed items. Although sequential recommendation has achieved great success in predicting future user interests, existing sequential recommendation methods do not fully exploit system exposure data. Most methods only model items that have been interacted with, while the large volume of exposed but non-interacted items is overlooked. Even methods that consider system exposure typically train the recommender using only the logged historical system exposure, without exploring unseen user interests.

Authors 9

1. Ziqi Zhao

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

2. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

3. Jiyuan Yang

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

4. Zihan Wang

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, Netherlands

5. Liu Yang

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

6. Pengjie Ren

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

7. Zhumin Chen

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

8. Maarten de Rijke

   University of Amsterdam

   Affiliation as printed

   Informatics Institute, University of Amsterdam, Amsterdam, Netherlands

9. Xin Xin

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China