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 compared to DeepCoNN model, and from 0.83 compared to TransNets model. Also, on two large categories, namely AZ-CSJ and AZ-Mov, our model achieves relative improvements of 8.08 the DeepCoNN model, and relative improvements of 1.74 the TransNets model, respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/16/2021

A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System

User and item reviews are valuable for the construction of recommender s...
research
04/07/2017

TransNets: Learning to Transform for Recommendation

Recently, deep learning methods have been shown to improve the performan...
research
10/07/2022

Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems

Given a user's input text, text-matching recommender systems output rele...
research
07/08/2019

Joint Neural Collaborative Filtering for Recommender Systems

We propose a J-NCF method for recommender systems. The J-NCF model appli...
research
12/20/2016

User Bias Removal in Review Score Prediction

Review score prediction of text reviews has recently gained a lot of att...
research
01/30/2018

TransRev: Modeling Reviews as Translations from Users to Items

The text of a review expresses the sentiment a customer has towards a pa...
research
12/08/2020

TADO: Time-varying Attention with Dual-Optimizer Model

The review-based recommender systems are commonly utilized to measure us...

Please sign up or login with your details

Forgot password? Click here to reset