基于集合扩展的自回归会话推荐模型研究
Research on Autoregressive Session-Based Recommendation Models with Set Expansion
DOI: 10.12677/mos.2024.136573, PDF,   
作者: 俞天浩：上海理工大学管理学院，上海
关键词: 会话推荐；集合学习；推荐系统；Session-Based Recommendation； Set Learning； Recommendation System
摘要: 会话推荐作为推荐领域中一项具有挑战性的任务，旨在根据用户的匿名行为序列向其推荐后续项目。目前，几乎所有的会话推荐都依赖于交互的顺序来进行推荐。然而，实际的推荐结果显示，会话的交互顺序未必是可靠的。因此，本文提出了一种基于集合扩展方法能够不依赖交互顺序的会话推荐方法。在实现过程中，通过深度神经网络构造了一种简单的自回归会话推荐模型DSETRec，并在该模型上进行对比实验，验证了方法在集合上的有效性。此外，我们还将该模型与其他先进基线相比，也展示出了较为良好的推荐效果。
Abstract: Session-based recommendation (SBR), a challenging task in the recommendation domain, aims to recommend subsequent items to users based on their anonymous behavior sequences. Currently, nearly all session-based recommendations rely on the order of interactions for making recommendations. However, real-world recommendation results indicate that the interaction order in a session may not always be reliable. Therefore, this paper proposes a session-based recommendation method based on a set extension approach that does not depend on interaction order. In the implementation, we constructed a simple autoregressive session-based recommendation model, DSETRec, using deep neural networks. Through comparative experiments on this model, we validated the effectiveness of the method on set data. Additionally, we compared this model with other advanced baselines, demonstrating its relatively strong recommendation performance.
文章引用：俞天浩. 基于集合扩展的自回归会话推荐模型研究[J]. 建模与仿真, 2024, 13(6): 6259-6273. https://doi.org/10.12677/mos.2024.136573

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