Modeling Temporal Dynamics of User Preferences in Movie Recommendation

Hamidreza Tahmasbi, Mehrdad Jalali, H. Shakeri
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引用次数: 6

Abstract

Users in movie recommender systems are likely to change their preferences over time. Modelling the temporal dynamics of user preferences is essential for improving the recommendation accuracy. In this paper, we propose an approach to model temporal dynamics of user preferences in movie recommendation systems based on a coupled tensor factorization framework. We weigh the past user preferences and decrease their importance gradually by introducing an individual time decay factor for each user according to the rate of his preference dynamics. We exploit users' demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences to generate movie recommendations. Our experiments on the public benchmark dataset, MovieLens show that our model outperforms other competitive methods and is more capable of alleviating the problems of cold-start and data sparsity.
电影推荐中用户偏好的时间动态建模
随着时间的推移,电影推荐系统的用户可能会改变他们的偏好。对用户偏好的时间动态建模对于提高推荐的准确性至关重要。在本文中,我们提出了一种基于耦合张量分解框架的电影推荐系统中用户偏好的时间动态建模方法。我们根据用户的偏好动态速率,为每个用户引入单个时间衰减因子,对过去的用户偏好进行加权,并逐渐降低其重要性。我们利用用户的人口统计数据以及随着时间的推移提取的用户之间的相似性,旨在增强关于用户偏好动态的先验知识,以及过去加权的用户偏好来生成电影推荐。我们在公共基准数据集MovieLens上的实验表明,我们的模型优于其他竞争方法,并且更有能力缓解冷启动和数据稀疏性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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