用隐半马尔可夫模型捕捉用户兴趣持续时间差异，提升推荐准确性。
Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach
- 引入隐半马尔可夫模型，建模用户在不同兴趣状态下的停留时长差异。
- 在三个真实数据集上显著优于主流时序与静态推荐方法。
- 适合关注兴趣动态变化、需精准建模行为持久性的推荐系统研究者。
推荐系统通过分析用户行为，为用户提供书籍、教育资料和商品建议。现实中，用户偏好随时间演变，促使研究向时序推荐系统发展。然而，现有方法对时间信息的处理仍较粗略。本文提出一种隐半马尔可夫模型，用于追踪用户兴趣的变化过程。该模型能捕捉用户在（潜在）兴趣状态中停留时间的异质性，更准确地刻画用户兴趣的多样性与持续性。我们推导了期望最大化算法以估计模型参数并预测用户行为。在三个真实数据集上的实验表明，本模型显著优于当前最先进的时序推荐与静态基准方法。进一步分析显示，性能提升与状态持续时间的异质性及用户兴趣漂移密切相关。
原文摘要 · Abstract (English)
Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users' preferences often change over time, leading to studies on time-dependent recommender systems. However, most existing approaches that deal with time information remain primitive. In this paper, we extend existing methods and propose a hidden semi-Markov model to track the change of users' interests. Particularly, this model allows for capturing the different durations of user stays in a (latent) interest state, which can better model the heterogeneity of user interests and focuses. We derive an expectation maximization algorithm to estimate the parameters of the framework and predict users' actions. Experiments on three real-world datasets show that our model significantly outperforms the state-of-the-art time-dependent and static benchmark methods. Further analyses of the experiment results indicate that the performance improvement is related to the heterogeneity of state durations and the drift of user interests in the dataset.
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