Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation
Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation
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DOI：
10.1109/tnnls.2019.2909432
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发表时间：
2020-03
影响因子：
10.4
通讯作者：
Jiayu Han;Lei Zheng;Yuanbo Xu;Bangzuo Zhang;Fuzhen Zhuang;Philip S. Yu;Wanli Zuo
中科院分区：
文献类型：
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作者：
Jiayu Han;Lei Zheng;Yuanbo Xu;Bangzuo Zhang;Fuzhen Zhuang;Philip S. Yu;Wanli Zuo
在现有的推荐系统中，矩阵分解（MF）被广泛应用于通过将用户-项目评分映射到低维潜在向量空间来对用户偏好和项目特征进行建模。然而，MF忽略了个体多样性，即用户对不同未评级项目的偏好通常是不同的。 MF提取的用户偏好因子的固定表示无法很好地建模个体多样性，从而导致重复且不准确的推荐。为此，我们提出了一种称为自适应深度潜在因子模型（ADLFM）的新型潜在因子模型，它根据所考虑的特定项目自适应地学习用户的偏好因子。我们提出了一种新颖的用户表示方法，该方法源自他们的评分项目描述，而不是原始的用户项目评分。在此基础上，我们进一步提出了一个带有注意力因子的深度神经网络框架来学习用户的自适应表示。对 Amazon 数据集的大量实验表明，ADLFM 的性能大大优于最先进的基线。此外，进一步的实验表明，注意力因素确实对我们的方法做出了很大的贡献。
In the existing recommender systems, matrix factorization (MF) is widely applied to model user preferences and item features by mapping the user-item ratings into a low-dimension latent vector space. However, MF has ignored the individual diversity where the user’s preference for different unrated items is usually different. A fixed representation of user preference factor extracted by MF cannot model the individual diversity well, which leads to a repeated and inaccurate recommendation. To this end, we propose a novel latent factor model called adaptive deep latent factor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration. We propose a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings. Based on this, we further propose a deep neural networks framework with an attention factor to learn the adaptive representations of users. Extensive experiments on Amazon data sets demonstrate that ADLFM outperforms the state-of-the-art baselines greatly. Also, further experiments show that the attention factor indeed makes a great contribution to our method.