Automated Disentangled Sequential Recommendation with Large Language Models

作者：Xin Wang, Hong Chen, Zirui Pan, Yuwei Zhou, Chaoyu Guan, Lifeng Sun, Wenwu Zhu · 发表于：ACM Transactions on Information Systems · 年份：2024 · DOI：10.1145/3675164 · 被引用次数：13 · 研究领域：Recommender Systems and Techniques、Topic Modeling、Advanced Graph Neural Networks

Sequential recommendation aims to recommend the next items that a target user may have interest in based on the user’s sequence of past behaviors, which has become a hot research topic in both academia and industry. In the literature, sequential recommendation adopts a Sequence-to-Item or Sequence-to-Sequence training strategy, which supervises a sequential model with a user’s next one or more behaviors as the labels and the sequence of the past behaviors as the input. However, existing powerful sequential recommendation approaches employ more and more complex deep structures such as Transformer in order to accurately capture the sequential patterns, which heavily rely on hand-crafted designs on key attention mechanism to achieve state-of-the-art performance, thus failing to automatically obtain the optimal design of attention representation architectures in various scenarios with different data. Other works on classic automated deep recommender systems only focus on traditional settings, ignoring the problem of sequential scenarios. In this article, we study the problem of automated sequential recommendation, which faces two main challenges: (1) How can we design a proper search space tailored for attention automation in sequential recommendation, and (2) How can we accurately search effective attention representation architectures considering multiple user interests reflected in the sequential behavior. To tackle these challenges, we propose an automated disentangled sequen...