The main task of personalized recommendation is capturing users' interests based on their historical behaviors. Most of recent advances in recommender systems mainly focus on modeling users' preferences accurately using deep learning based approaches. There are two important properties of users' interests, one is that users' interests are dynamic and evolve over time, the other is that users' interests have different resolutions, or temporal-ranges to be precise, such as long-term and short-term preferences. Existing approaches either use Recurrent Neural Networks (RNNs) to address the drifts in users' interests without considering different temporal-ranges, or design two different networks to model long-term and short-term preferences separately. This paper presents a multi-resolution interest fusion model (MRIF) that takes both properties of users' interests into consideration. The proposed model is capable to capture the dynamic changes in users' interests at different temporal-ranges, and provides an effective way to combine a group of multi-resolution user interests to make predictions. Experiments show that our method outperforms state-of-the-art recommendation methods consistently.

中文翻译：

---

MRIF：推荐的多分辨率兴趣融合

个性化推荐的主要任务是根据用户的历史行为来捕捉用户的兴趣。推荐系统的最新进展主要集中在使用基于深度学习的方法准确地建模用户的偏好。用户的兴趣有两个重要的属性，一是用户的兴趣是动态的，随着时间的推移而演变，二是用户的兴趣有不同的分辨率，或者准确地说是时间范围，比如长期和短期——术语偏好。现有方法要么使用循环神经网络 (RNN) 来解决用户兴趣的偏差，而无需考虑不同的时间范围，要么设计两个不同的网络来分别对长期和短期偏好进行建模。本文提出了一种多分辨率兴趣融合模型（MRIF），它考虑了用户兴趣的两个属性。所提出的模型能够捕捉用户兴趣在不同时间范围内的动态变化，并提供一种有效的方法来组合一组多分辨率的用户兴趣进行预测。实验表明，我们的方法始终优于最先进的推荐方法。