A Hybrid Recommendation Model Based on Probabilistic Matrix Factorization and Sentiment Analysis

Abstract

In recommender systems probabilistic matrix factorization model has been examined and showed to give promising recommendation but it only takes ratings into consideration and has long been criticized for its inability to provide explainable recommendation On the other hand because we can unearth users' preferred features from review text review text should be viewed as an essential element in recommender systems And when it comes to understanding users' preferred features sentiment analysis methods report great performance on extracting product features and sentiment toward product attributes However most sentiment analysis methods are incapable of ranking user preferred features Therefore we incorporate sentiment analysis and probabilistic matrix factorization model to find user preferred features In addition we assume that when users give products or services ratings and review they behave in similar fashions because human behavior tend to exhibit a certain level of consistency Under such assumption we focus on both ratings and reviews In this study we learn user preferred features by fusing probabilistic matrix factorization with sentiment analysis and experiments show that our model is able attain high accuracy recommendation and also take a step closer to solving the problem of matrix factorization technique which is its inability to provide interpretable recommendation

Date of Award	2016 Aug 22
Original language	English
Supervisor	Ren-Shiou Liu (Supervisor)