Accurately forecasting user’s purchase intention over time is a huge challenge for personalized recommend systems, of which a critical problem is how to model the changes of user preference and temporal correlation of items. In this paper, aiming at addressing this question, we first introduce attention flow network to model users’ purchase records by leveraging attention flow that describes the changing process of purchase intention. Then based on the attention flow network and individuals’ attention flows, we propose a novel personalized recommendation algorithm named Attention Flow Network based Personalized Recommendation (AFNPR). Our method integrates all the purchase sequences of users into a weighted attention flow network, and recommends items based on transition probabilities related to attention flow network with user’s attention decay, which is efficient in linear time. The experiments demonstrate its superior performance on several real datasets.

对于个性化推荐系统，随着时间的推移准确地预测用户的购买意愿是一个巨大的挑战，其中的关键问题是如何对用户偏好和商品的时间相关性的变化进行建模。本文针对这一问题，首先介绍了注意力流网络，通过利用描述购买意愿变化过程的注意力流来建模用户的购买记录。然后基于注意力流网络和个人注意力流，提出了一种新颖的个性化推荐算法，即基于注意力流网络的个性化推荐（AFNPR）。我们的方法将用户的所有购买顺序集成到加权关注流网络中，并根据与关注流网络相关的转移概率（用户注意力衰减）推荐商品，在线性时间上很有效。实验证明了它在几个真实数据集上的优越性能。