Multi-interest Sequence Modeling for Recommendation with Causal Embedding

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Multi-interest Sequence Modeling for Recommendation with Causal EmbeddingCaiqi Sun, Penghao Lu, Lei Cheng, Zhenfu Cao, Xiaolei Dong, Yili Tang, Jun Zhou, and Linjian MoCaiqi Sun, Penghao Lu, Lei Cheng, Zhenfu Cao, Xiaolei Dong, Yili Tang, Jun Zhou, and Linjian Mopp.406 - 414Chapter DOI:https://doi.org/10.1137/1.9781611977172.46PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Recent methods in sequential recommendation focus on learning multi-interest embedding vectors from a user's behavior sequence for the next-item recommendation. However, behavior sequential data may result from users' conformity towards popular items, which entangles users' real interests and tends to recommend popular items by using interest embeddings. In this paper, we propose a novel multi-interest framework with causal embedding for sequential recommendation, called MiceRec. Specifically, we first obtain two embedding layers from behavior sequence by assigning items with separate embeddings for interest and conformity, then extract multiple pure interests from one embedding layer, while the other for users' conformity extraction. According to the colliding effect of causal inference, we mine cause-specific data for training causal embeddings. Our framework significantly outperforms state-of-the-art solutions on two real-world datasets1. We further demonstrate that the learned multi-interest embeddings successfully separate from each other, and show that conformity information is almost squeezed out from interest embeddings. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:recommender system, sequential recommendation, causal embedding, multi-interest framework