# Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
## 1 Introduction  
With the rapid development of the Internet, the problem of information overload has become increasingly serious [1; 2; 3]. As an information filtering system, the recommendation system utilizes rating prediction methods to predict users' rating of items and generates a ranking list of items according to each user's preferences for personalized recommendations. Matrix factorization (MF) [4] is widely adopted for rating prediction in the recommendation field. MF represents user preferences and item attributes as vectors of potential factors in the joint potential space by factorizing the user-item interaction matrix. However, in the absence of user historical behavior, MF has a data sparsity problem and cannot make effective recommendations. To solve the data sparsity problem, researchers have begun to use review information to improve the rating prediction quality [5; 6; 7]. Recently, some efforts have been made in reviews using deep learning models. For example, [PERSON] et al. [5] adopted a CNN (convolutional neural network) to extract potential features from user reviews and item reviews to simulate users and items. [PERSON] et al. [6] used an attention mechanism to evaluate the importance of each review and selected the highly useful reviews. By adopting a coattention network at the review level and word level, the MPCN model proposed by [PERSON] et al. [7] can dynamically select important reviews (words) for the target user according to the target item and dynamically select important reviews (words) for the target item according to the target user. Although these studies have achieved excellent results, there are still some problems. [PERSON] et al. [5] concatenate all reviews into a document as input without considering that the quality of reviews written bydifferent users is different. NARRE, proposed by [PERSON] et al. [6], only judges the usefulness within the user and item reviews but does not consider the interaction between users and items. [PERSON] et al. [7] utilized only review information to represent user preferences and item attributes and neglected to utilize additional auxiliary information to enhance the representations of users (items). Some previous works adopted the attention mechanism to obtain useful information from users (item) reviews without considering the interactions between users and items. However, if the target item is not relevant to the item described by the selected review, the information of the selected review has no referential value for rating prediction. Therefore, modeling the interactions of user reviews and item reviews can better identify useful review information. In addition, in review-based neural recommender models, the introduction of ID embedding can reveal the potential relationships between entities and enhance user (item) representations. However, some existing approaches only string review features and ID embedding together and cannot effectively merge them. Inspired by the above, a multipointer coattention recommendation model (MPCAR) with gated fusion between ID embedding and reviews aiming at learning the comprehensive representations of users and items is proposed. The main contributions of this paper are as follows:  
* The proposed MPCAR model first uses a review gating mechanism to extract important reviews from the input sequence (user reviews and item reviews). Then, it uses review-level coattention and a multipointer learning scheme to extract the most informative reviews and models these reviews at the word level to capture richer interactions;
* ID embedding that reveals the identity of users and items is introduced. In addition, a gated neural fusion layer is designed to effectively integrate ID embedding and review features to generate the final comprehensive representation of users (items). Finally, the final representations of users and items are fed into a factorization machine (FM) to predict users' ratings of the target items;
* The MPCAR model was evaluated on real datasets from Amazon in ten different domains.