AGTR: Adversarial Generation of Target Review for Rating Prediction
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AGTR: Adversarial Generation of Target Review for Rating Prediction Huilin Yu1 · Tieyun Qian1 · Yile Liang1 · Bing Liu1 Received: 28 April 2020 / Revised: 17 August 2020 / Accepted: 24 August 2020 / Published online: 17 September 2020 © The Author(s) 2020
Abstract Recent years have witnessed a growing trend of utilizing reviews to improve the performance and interpretability of recommender systems. Almost all existing methods learn the latent representations from the user’s and the item’s historical reviews and then combine these two representations for rating prediction. The fatal limitation in these methods is that they are unable to utilize the most predictive review of the target user for the target item since such a review is not available at test time. In this paper, we propose a novel recommendation model, called AGTR, which can generate the unseen target review with adversarial training for rating prediction. To this end, we develop a unified framework to combine the rating tailored generative adversarial nets for synthetic review generation and the neural latent factor module using the generated target review along with historical reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks. Keywords Recommender systems · Review aware recommendation · Generative adversarial network
1 Introduction A user’s rating indicates his/her attitude toward an purchased item. Rating prediction aims to predict the user’s ratings on unrated items which may reflect his/her potential interests on these items. Collaborative filtering (CF) approaches, which mainly depend on historical ratings, have aroused great research interests and become the dominant method in recommender systems. As a typical CF technique, matrix factorization (MF) learns the latent features of users and items by decomposing the user-item rating matrix and then uses these two feature vectors to predict the rating that the user would assign to the item. MF is the most widely used technique for rating prediction. However, MF-based methods suffer from the data * Tieyun Qian [email protected] Huilin Yu [email protected] Yile Liang [email protected] Bing Liu [email protected] 1
School of Computer Science, Wuhan University, Wuhan, Hubei, China
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sparsity problem and the predicted rating lacks the interpretability on why the user gives high or low scores. To tackle these issues, textual reviews have become a key complementary data source to enhance the performance and interpretation of the rating prediction task [1, 8, 26, 41]. In particular, due to the power of nonlinear combination of different types of information, impressive progress has been made by applying deep neural networks to this problem [3, 4, 6, 24, 35, 42]. The pioneering work by Zheng et al. [42] proposed a DeepCoNN model to represent both users and items in a joint manner using all the reviews of users and items. As proven in
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