IEEE Trans Neural Netw Learn Syst. 2020 Mar;31(3):737-748. doi: 10.1109/TNNLS.2019.2909432. Epub 2019 Jun 12.
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.
在现有的推荐系统中，矩阵分解（MF）通过将用户-项目评分映射到低维潜在向量空间，被广泛应用于对用户偏好和项目特征建模。然而，MF 忽略了个体多样性，即用户对不同未评分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形式不能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，我们提出了一种新的潜在因子模型，称为自适应深度潜在因子模型（ADLFM），它可以根据特定的考虑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，该方法源于他们对已评分项目描述的信息，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种具有注意力因子的深度神经网络框架，用于学习用户的自适应表示。在亚马逊数据集上的大量实验表明，ADLFM 大大优于最先进的基线方法。此外，进一步的实验表明，注意力因子确实对我们的方法做出了巨大贡献。
