# An Adversarial Deep Hybrid Model for Text-Aware Recommendation with Convolutional Neural Networks
## 1 Introduction  
In recent years, many commercial websites have employed recommender systems to provide better services for their customers. Recommender systems aim to match items to users through exploring the hidden connections between users and items. One of the most successful techniques for recommender systems is collaborative filtering based upon Matrix Factorization (MF) [1; 2; 3; 4; 5; 6; 7]. A standard MF-based approach aims to discover latent similarities among users and items from user-item interaction data, such as clicks and ratings, by learning user and item vectors whose dot products predict the users' preference scores over items. Although these MF-based methods have been shown to achieve promising recommendation results, they typically suffer from data sparsity and cold-start problems. In real-world scenarios, a user commonly interacts with only a tiny number of items in the system and an item is also consumed by only a minority of users. As a result, standard MF-based methods may fail to capture effective collaborative information based on the extremely sparse user-item interactions In addition, it is practically infeasible for matrix factorization to obtain meaningful latent factors of new users or items which are associated with no interaction data (a.k.a., cold-start problem). To address these issues, plentyof work have attempted to build hybrid recommendation models by exploiting auxiliary content data information to aid MF-based collaborative filtering [8; 9; 10; 11; 12; 13; 14; 15; 16]. A common approach to building a hybrid model is to bridge together the content feature modeling and matrix factorization. These methods range from non-DL (Deep Learning) to DL-based models, with their major difference lying in their ways of learning auxiliary features from the content data. Deep hybrid models [9; 10; 13; 14; 15; 16], by using DL techniques to extract deep content features, have recently shown superior performance than traditional non-DL based models, which adopts shallow representation learning techniques, such as LDA [8; 11; 17]. In principle, these hybrid models can be built from a probabilistic perspective, where content features are incorporated as priors into MF to regularize the latent user and item vectors [8; 9; 16; 18]. However, this probabilistic treatment involves manually tuning additional regularization parameters and limits the use of other non-MF based collaborative filtering methods. Besides, since the exact inference for latent variables is usually intractable, the estimation of model parameters has to rely on approximation methods, such as maximum a posteriori inference (i.e., maximum likelihood estimation with regularization terms). Moreover, most existing deep hybrid models detect the content features through auto-encoders [9; 13; 15]. However, auto-encoders assume the bag-of-words model by ignoring contextual text information such as surrounding words and word orders. Thus, they are unable to fully capture document features in text-aware recommendation scenarios where items are commonly associated with text information such as reviews. In order to address the aforementioned problems, this paper _first_ develops an adversarial training framework to learn a deep hybrid recommendation model. Specifically, we specify the recommender model as a generator to generate item ranking pairs while introducing an adversarial discriminator that evolves to discriminate between the real data and the generated data. Compared with maximum likelihood estimation, this indirect approach to learning the probability distribution offers more flexibility and has shown promising results in learning latent variable models [19; 20; 21; 22]. Maximum likelihood estimation is theoretically equivalent to maximizing the Kullback-Leibler divergence between the data distribution and modeled distribution, whereas the adversarial paradigm corresponds to maximizing the Jensen-Shannon divergence, which may thus boost the capability of learning the distributional features over the collaborative patterns [19; 21].