Abstract
Recently, the interaction information from reviews has been modeled to acquire representations between users and items and improve the sparsity problem in recommendation systems. Reviews are more responsive to information about users’ preferences for the different aspects and attributes of items. However, how to better construct the representation of users (items) still needs further research. Inspired by the interaction information from reviews, auxiliary ID embedding information is used to further enrich the word-level representation in the proposed model named MPCAR. In this paper, first, a multipointer learning scheme is adopted to extract the most informative reviews from user and item reviews and represent users (items) in a word-by-word manner. Then, users and items are embedded to extract the ID embedding that can reveal the identity of users (items). Finally, the review features and ID embedding are input to the gated neural network for effective fusion to obtain richer representations of users and items. We randomly select ten subcategory datasets from the Amazon dataset to evaluate our algorithm. The experimental results show that our algorithm can achieve the best results compared to other recommendation approaches.
Highlights
With the rapid development of the Internet, the problem of information overload has become increasingly serious [1,2,3]
The ConvMF model [31] proposed by Kim et al uses a CNN to process the text information of items, learns the hidden features of items, and integrates the features into a rating matrix decomposed by the PMF model to improve the rating prediction accuracy
MPCAR is a neural network model consisting of a review feature learning module, a user embedding module, a gated fusion layer, and a prediction layer (FM)
Summary
With the rapid development of the Internet, the problem of information overload has become increasingly serious [1,2,3]. By adopting a coattention network at the review level and word level, the MPCN model proposed by Tay 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 These studies have achieved excellent results, there are still some problems. 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 proposed MPCAR model first uses a review gating mechanism to extract important reviews from the input sequence (user reviews and item reviews) 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;. The experimental results of the model outperformed those of existing popular methods
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