Abstract
As there is a huge amount of information on the Internet, people have difficulty in sorting through it to find the required information; thus, the information overload problem becomes a significant issue for users and online businesses. To resolve this problem, many researchers and applications have proposed recommender systems, which apply user-based collaborative filtering, meaning it only considers the users’ rating history to analyze their preferences. However, users’ text data may contain users’ preferences or sentiment information, and such information can be used to analyze users’ preferences more precisely. This work proposes a method called the aspect-based deep learning rating prediction method (ADLRP), which can extract the aspects, sentiment, and semantic features from users’ and items’ reviews. Then, the deep learning method is used to generate users’ and items’ latent factors. According to these three features, the matrix factorization method is applied to make rating predictions for items. The experimental results show that the proposed method performs better than the traditional rating prediction methods and conventional artificial neural networks. The proposed method can precisely and efficiently extract the sentiments and semantics of each aspect from review texts and enhance the prediction performance of rating predictions.
Highlights
Due to the development of Web 2.0 and the popularity of mobile devices, everyone is free to share their opinions and publish articles on the Internet
To address the problem in the existing studies, this work proposes an aspect-based deep learning rating prediction method (ADLRP) for review websites, which consists of four main components: aspect detection, sentiment analysis, semantic analysis, and rating prediction
A recommender system can automatically analyze user preferences and recommend items that may be of interest to future users to solve the problem of information overload
Summary
Due to the development of Web 2.0 and the popularity of mobile devices, everyone is free to share their opinions and publish articles on the Internet. Using deep learning or neural network methods can improve the performance and accuracy of both prediction and classification These studies only analyzed the semantics or sentiments in the reviews, without considering user-preferred aspects implied in user reviews. Besides users’ rating data, developing the methods for identifying user-preference aspects, semantic and sentiment features hidden in text reviews, and combining the implicit and explicit information in a recommendation method help to generate insights into user preferences and product features. We propose a novel ADLRP method which could effectively extract the aspect, sentiment, and semantic features from text reviews and combine these features with ratings for rating prediction Our method integrates both implicit and explicit information to analyze user preferences and product features, and achieves better predictive performance. We discuss the common deep learning methods
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