1 research outputs found
Deep Neural Review Text Interaction for Recommendation Systems
Users' reviews contain valuable information which are not taken into account
in most recommender systems. According to the latest studies in this field,
using review texts could not only improve the performance of recommendation,
but it can also alleviate the impact of data sparsity and help to tackle the
cold start problem. In this paper, we present a neural recommender model which
recommends items by leveraging user reviews. In order to predict user rating
for each item, our proposed model, named MatchPyramid Recommender System
(MPRS), represents each user and item with their corresponding review texts.
Thus, the problem of recommendation is viewed as a text matching problem such
that the matching score obtained from matching user and item texts could be
considered as a good representative of their joint extent of similarity. To
solve the text matching problem, inspired by MatchPyramid (Pang, 2016), we
employed an interaction-based approach according to which a matching matrix is
constructed given a pair of input texts. The matching matrix, which has the
property of hierarchical matching patterns, is then fed into a Convolutional
Neural Network (CNN) to compute the matching score for the given user-item
pair. Our experiments on the small data categories of Amazon review dataset
show that our proposed model gains from 1.76% to 21.72% relative improvement
compared to DeepCoNN model, and from 0.83% to 3.15% relative improvement
compared to TransNets model. Also, on two large categories, namely AZ-CSJ and
AZ-Mov, our model achieves relative improvements of 8.08% and 7.56% compared to
the DeepCoNN model, and relative improvements of 1.74% and 0.86% compared to
the TransNets model, respectively.Comment: 19 pages, 3 figure