A New Approach Item Rating Data Mining on the Recommendation System
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ORIGINAL RESEARCH
A New Approach Item Rating Data Mining on the Recommendation System Anh Nguyen Thi Dieu1 · Thanh Nguyen Vu1 · Tuan Dinh Le2 Received: 16 April 2020 / Accepted: 28 September 2020 © Springer Nature Singapore Pte Ltd 2020
Abstract Collaborative filtering (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 efficiency of the recommendation system. This paper proposes a new approach to analyze rating item and input the implicit effect 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 filtering · Implicit effect · Matrix factorizations · Matrix user · Trustbased recommender · Linear regression · Machine learning
Introduction Recommendation systems have been using in many application scenarios. For example, in e-commerce, the recommendation 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 filtering, collaborative filtering, and hybrid. Content filtering (CF) based on purchase history, views user information, thereby suggesting products with content similar to buyers’ needs. Some popular techniques currently used for content filtering are Bag of the word, TF-IDF (term frequency-inverse document frequency), Graph, Grid, TF (term frequency), VSM (Vector Space Model) [2].
* Thanh Nguyen Vu [email protected] Anh Nguyen Thi Dieu [email protected] Tuan Dinh Le [email protected] 1
Van Hien University, Ho Chi Minh City, Viet Nam
Long An University of Economics and Industry, Long An province, Viet Nam
2
Collaborative filtering 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 identifies the similarity of the objects through adjacent measurements. Current techniques for collaborative filtering are: Pearson correlation (CORR), Cosine (COS), Adjust Cosine (ACOS), Constrained Correlation (CCORR), Mean square Difference (MSD), Euclidean (EUC), and SM SING (singularities) [2, 4]. Hybrid methods are a combination of content filtering and collaborative filtering, relying on the advantages of one technique to overcome the disadvantages of the other. For example, collaborative filtering 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 simply do it when the prediction for new items based on user descriptions is available and straightforward. The rating
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