Optimal Rating Prediction in Recommender Systems

Abstract Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

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The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems

PLoS ONE ◽

10.1371/journal.pone.0154848 ◽

2016 ◽

Vol 11 (5) ◽

pp. e0154848 ◽

Cited By ~ 10

Author(s):

Waleed Reafee ◽

Naomie Salim ◽

Atif Khan

Keyword(s):

Recommender Systems ◽

Social Relation ◽

Social Recommender Systems ◽

Rating Prediction

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Towards Comprehensive Approaches For The Rating Prediction Phase In Memory-based Collaborative Filtering Recommender Systems

Information Sciences ◽

10.1016/j.ins.2021.12.123 ◽

2022 ◽

Author(s):

Le Nguyen Hoai Nam

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Rating Prediction

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Overlapping Community Regularization for Rating Prediction in Social Recommender Systems

Proceedings of the 9th ACM Conference on Recommender Systems - RecSys '15 ◽

10.1145/2792838.2800171 ◽

2015 ◽

Cited By ~ 37

Author(s):

Hui Li ◽

Dingming Wu ◽

Wenbin Tang ◽

Nikos Mamoulis

Keyword(s):

Recommender Systems ◽

Social Recommender Systems ◽

Overlapping Community ◽

Rating Prediction

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Leveraging Distrust Relations to Improve Bayesian Personalized Ranking

Information ◽

10.3390/info9080191 ◽

2018 ◽

Vol 9 (8) ◽

pp. 191 ◽

Cited By ~ 1

Author(s):

Yangjun Xu ◽

Ke Xu ◽

Yi Cai ◽

Huaqing Min

Keyword(s):

Recommender Systems ◽

Experimental Results ◽

Performance Evaluations ◽

Bayesian Personalized Ranking ◽

Rating Prediction ◽

Novel Model ◽

Personalized Ranking ◽

Trust Information

Distrust based recommender systems have drawn much more attention and became widely acceptable in recent years. Previous works have investigated using trust information to establish better models for rating prediction, but there is a lack of methods using distrust relations to derive more accurate ranking-based models. In this article, we develop a novel model, named TNDBPR (Trust Neutral Distrust Bayesian Personalized Ranking), which simultaneously leverages trust, distrust, and neutral relations for item ranking. The experimental results on Epinions dataset suggest that TNDBPR by leveraging trust and distrust relations can substantially increase various performance evaluations including F1 score, AUC, Precision, Recall, and NDCG.

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