Gumble Softmax For User Behavior Modeling
Weiqi Shao, Xu Chen, Jiashu Zhao, Long Xia, Dawei Yin

TL;DR
This paper introduces RDRSR, a sequential recommendation model that dynamically adjusts the number of user interest representations over time using Gumbel-Softmax, bi-directional self-attention, and interest allocation modules.
Contribution
It proposes a novel approach to model evolving user interests with a dynamic number of representations, improving personalization in recommendation systems.
Findings
Outperforms existing models on real-world datasets.
Effectively captures evolving user interests.
Demonstrates the importance of dynamic interest modeling.
Abstract
Recently, sequential recommendation systems are important in solving the information overload in many online services. Current methods in sequential recommendation focus on learning a fixed number of representations for each user at any time, with a single representation or multi representations for the user. However, when a user is exploring items on an e-commerce recommendation system, the number of this user's hobbies may change overtime (e.g. increase/reduce one more interest), affected by the user's evolving self needs. Moreover, different users may have various number of interests. In this paper, we argue that it is meaningful to explore a personalized dynamic number of user interests, and learn a dynamic group of user interest representations accordingly. We propose a sequential model with dynamic number of representations for recommendation systems (RDRSR). Specifically, RDRSR…
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Taxonomy
TopicsRecommender Systems and Techniques · Complex Network Analysis Techniques · Advanced Bandit Algorithms Research
