Abstract User-generated product reviews contain a lot of valuable information including users’ opinions on products and product features that is not fully exploited by the current recommendation models. Similarly, the metadata information related to the products, about the reviews and about the users who have written the reviews has rarely been exploited for recommender systems. These heterogeneous information sources have the potential to alleviate the cold start and sparsity problems and improve the quality of recommendations. In this paper, we present a joint deep recommendation model (JDRM) that consists of two parallel neural networks, learning lower-order feature interactions of users and items separately and higher-order feature interactions jointly using a shared last layer. Each of the networks is further composed of two sub-networks. One of the sub-networks focus on exploiting product reviews (of user/item) and the other sub-network learns user preferences/items properties leveraging metadata information along with the ratings. The learned latent features in each network are concatenated, thus producing the user and item latent feature vectors. We combine the two networks by introducing a shared layer on the top, which is a dense fully connected layer used to learn higher level latent features obtained from the two networks and produces final ratings. Extensive experiments on real-world datasets demonstrate that JDRM significantly outperforms state of the art recommendation models.

中文翻译：

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利用评论和元数据信息的联合深度推荐模型

摘要 用户生成的产品评论包含许多有价值的信息，包括用户对产品的意见和产品功能，这些信息尚未被当前推荐模型充分利用。同样，与产品、评论和撰写评论的用户相关的元数据信息很少被推荐系统利用。这些异构信息源有可能缓解冷启动和稀疏问题并提高推荐质量。在本文中，我们提出了一个联合深度推荐模型（JDRM），它由两个并行神经网络组成，分别学习用户和项目的低阶特征交互，并使用共享的最后一层联合学习高阶特征交互。每个网络进一步由两个子网络组成。其中一个子网络专注于利用（用户/项目的）产品评论，另一个子网络利用元数据信息和评级来学习用户偏好/项目属性。每个网络中学习到的潜在特征被连接起来，从而产生用户和项目的潜在特征向量。我们通过在顶部引入一个共享层来组合两个网络，这是一个密集的全连接层，用于学习从两个网络获得的更高级别的潜在特征并产生最终评级。对真实世界数据集的大量实验表明，JDRM 显着优于最先进的推荐模型。每个网络中学习到的潜在特征被连接起来，从而产生用户和项目的潜在特征向量。我们通过在顶部引入一个共享层来组合两个网络，这是一个密集的全连接层，用于学习从两个网络获得的更高级别的潜在特征并产生最终评级。对真实世界数据集的大量实验表明，JDRM 显着优于最先进的推荐模型。每个网络中学习到的潜在特征被连接起来，从而产生用户和项目潜在特征向量。我们通过在顶部引入一个共享层来组合两个网络，这是一个密集的全连接层，用于学习从两个网络获得的更高级别的潜在特征并产生最终评级。对真实世界数据集的大量实验表明，JDRM 显着优于最先进的推荐模型。