Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

Abstract

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinionrich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.

Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review

“…Collaborative filtering-based recommendations obtain product recommendation lists by identifying a set of users who are similar to the current user. Based on this technique and online reviews, Wang and Wang [98] mined user opinions and mapped them to multicategory and single-category recommendations. However, the pursuit of extreme personalization of recommendation and marketing is prone to cause "information cocoon" problems.…”

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

“…Wei Wang and Hongwei Wang [21] have applied sentiment analysis technology to recommendation systems, calculated similarity through analyzing the preference polarity (positive and negative) of users for product features and improved the accuracy of the recommendation algorithm by increasing the precision of the nearest set of users. Gan [7] divided the user's sentiment into positive, negative, neutral and conflict types when analyzing the user's review text.…”

From Opinions to Recommendations

“…Their results show that user reviews provide the best source of information for movie recommendations, followed by movie genre data. Further, the authors in [71] leverage opinions mined from online reviews to enhance user preference models for use in collaborative recommender systems. Experiments indicate the approach outperforms baselines algorithms with respect to accuracy and recall.…”