Combining Opinion Mining with Collaborative Filtering

Improvement of Collaborative Filtering Recommendation System to Resolve Sparsity Problem using Combination of Clustering and Opinion Mining Methods

2019 International Seminar on Application for Technology of Information and Communication (iSemantic) ◽

10.1109/isemantic.2019.8884223 ◽

2019 ◽

Cited By ~ 1

Author(s):

W.P. Krisdhamara ◽

Bambang Pharmasetiawan ◽

Kusprasapta Mutijarsa

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Opinion Mining ◽

Mining Methods ◽

Sparsity Problem

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A review of a recommendation filtering system approach based on reliable sustainable opinion mining

Technological Sustainability ◽

10.1108/techs-09-2021-0012 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Imen Gmach ◽

Nadia Abaoub ◽

Rubina Khan ◽

Naoufel Mahfoudh ◽

Amira Kaddour

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Information Search ◽

Recommendation System ◽

Opinion Mining ◽

Information Filtering ◽

Recommendation Systems ◽

Content Filtering ◽

Content Type ◽

The Impact

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

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Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

Journal of Information Science ◽

10.1177/0165551517692955 ◽

2017 ◽

Vol 44 (3) ◽

pp. 331-344 ◽

Cited By ~ 15

Author(s):

Youdong Yun ◽

Danial Hooshyar ◽

Jaechoon Jo ◽

Heuiseok Lim

Keyword(s):

Collaborative Filtering ◽

Quantitative Data ◽

Prediction Accuracy ◽

Recommendation System ◽

Opinion Mining ◽

Recommendation Systems ◽

Product Recommendation ◽

Collaborative Filtering Algorithm ◽

Product Rating ◽

Positive Recommendation

The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

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