Vol.:(0123456789) SN Computer Science (2021) 2:2 https://doi.org/10.1007/s42979-020-00351-4 SN Computer Science ORIGINAL RESEARCH A New Approach Item Rating Data Mining on the Recommendation System Anh Nguyen Thi Dieu 1  · Thanh Nguyen Vu 1  · Tuan Dinh Le 2 Received: 16 April 2020 / Accepted: 28 September 2020 / Published online: 7 November 2020 © Springer Nature Singapore Pte Ltd 2020 Abstract Collaborative fltering (CF) in the recommendation system using user habits, behaviors, and item rating to recommend the products which suit customer’s needs. Therefore, analyzing user rating data is one of the factors that improve the efciency of the recommendation system. This paper proposes a new approach to analyze rating item and input the implicit efect of items rating to the recommendation system based on the TrustSVD model and matrix factorization (MF) techniques. The experimental results showed that our proposed solution achieves 18% better than the matrix factorization method and 15% the Multi-Relational Matrix Factorization method, respectively. Keywords Recommendation system · Collaborative fltering · Implicit efect · Matrix factorizations · Matrix user · Trust- based recommender · Linear regression · Machine learning Introduction Recommendation systems have been using in many applica- tion scenarios. For example, in e-commerce, the recommen- dation system analysis based on user’s interests, searching keywords, product reviews to make recommendations for users. Currently, three methods, which have been widely used in a recommendation system, are content fltering, col- laborative fltering, and hybrid. Content fltering (CF) based on purchase history, views user information, thereby suggesting products with content similar to buyers’ needs. Some popular techniques currently used for content fltering are Bag of the word, TF-IDF (term frequency-inverse document frequency), Graph, Grid, TF (term frequency), VSM (Vector Space Model) [2]. Collaborative fltering is a technique that determines a user’s interest in a new product based on previous products they rate, recommending similar products with consumer appreciation. The recommended system in this approach identifes the similarity of the objects through adjacent measurements. Current techniques for collaborative flter- ing are: Pearson correlation (CORR), Cosine (COS), Adjust Cosine (ACOS), Constrained Correlation (CCORR), Mean square Diference (MSD), Euclidean (EUC), and SM SING (singularities) [2, 4]. Hybrid methods are a combination of content fltering and collaborative fltering, relying on the advantages of one technique to overcome the disadvantages of the other. For example, collaborative fltering has a problem with a cold start, which is challenging to suggest for items that do not have a rating, while the content-based approach can sim- ply do it when the prediction for new items based on user descriptions is available and straightforward. The rating of the recommendation system is usually an integer value from 1 to 5. In the same evaluation index, the criteria for user ratings are diferent. For example, one hotel is rated 4* by both guest A and guest B. This rating consists of clean, service, attached utilities, location. Guest A may be more satisfed with the service in a hotel, but guest B likes the hotel’s utilities. For simplicity of calculation, this infor- mation is often missed from the recommendation system. * Thanh Nguyen Vu nguyenvt@vhu.edu.vn Anh Nguyen Thi Dieu anhntdgm@gmail.com Tuan Dinh Le le.tuan@daihoclongan.edu.vn 1 Van Hien University, Ho Chi Minh City, Viet Nam 2 Long An University of Economics and Industry, Long An province, Viet Nam