Handling Dynamic User Preferences Using Integrated Point and Distribution Estimations in Collaborative Filtering
提出一种结合矩阵分解点估计和隐马尔可夫模型分布估计的协同过滤方法，用于处理用户偏好随时间变化的问题，在三个基准数据集上表现更稳健。
Change in users’ preferences over time is a challenging issue in collaborative filtering models. Recent temporal recommender systems (RSs) use latent temporal features or probabilistic latent transition models. The models designed using latent temporal features remain confined to point estimation only, i.e., considering single predicted values (e.g., rating) and completely ignore the likelihood of other possible items being generated due to change in users’ preferences. The latent transitive models represent users’ preference over different states using distribution estimation but ignore user–item features. This article proposes an integrated approach for designing collaborative filtering models using both point and distribution estimation techniques together to address this issue. The matrix factorization-based point estimation strategy is applied to integrate user–item correlations and feature information, whereas the hidden Markov model is used for users’ preference distribution over time. Three benchmark datasets were used to evaluate and compare the proposed model’s performance with related state-of-the-art temporal RS models. The proposed model makes the system more robust in handling dynamic users’ preferences.