The Use of User Reviews to Improve Rating Prediction of Collaborative Filtering
Date Issued
2015
Date
2015
Author(s)
Zhang, Ya-Chiao
Abstract
Collaborative filtering (CF) is widely used in recommender systems. However, it suffers from cold-start problem and data sparsity problem. In this work, we take user reviews into consideration to help with CF performance. We combine sentiment of user reviews and predicted ratings of CF in rating prediction model through a source weighting function. The main idea is to use features to decide which source, the review or CF, is more reliable. There are three kinds of features in the model, collaborative features, user review features, and meta-path features. Unlike traditional user or item similarity, meta-path build links between users and items. The experiments show that meta-path has best performance among all features. We conduct experiments on rating prediction, the influence of different source quality, inconsistence of ratings and review sentiment, and feature importance analysis. The results show that our method outperforms compared methods on rating prediction and the source weighting function generates appropriate weights for sources given different kinds of source quality.
Subjects
Collaborative filtering
user reviews
recommender systems
meta-path
Type
thesis
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