A HYBRID FRAMEWORK FOR ENHANCING CORRELATION TO SOLVE COLD-START PROBLEM IN RECOMMENDER SYSTEMS
Abstract
The online shopping is becoming a trend in the age of digital technology. By using intelligent recommendations, the online shops or online retailers directly approach and meet customers’ demand easier than the physical stores. However, the online shopping still has its drawbacks, among a variety of diverse product types, sizes and design, customers need to browse and filter from a wide range of sub-categories to find the suitable products. That is why the justice system that collects customer information and products to make appropriate suggestions for each user is raised encouraged using on the commercial website. The purpose of this work aims at proposing a hybrid framework for enhancing correlation to solve cold-start problem in recommender systems. Experiments are performed using MovieLens dataset to make a realistic methodology.References
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[14] B.,Sarwar, G.,Karypis, J.,Konstan,and J.,Riedl (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). ACM.
[15] G.,Shani, and A.,Gunawardana (2011). Evaluating recommendation systems. Recommender Systems Handbook, 257-297.
Published
2014-12-17
How to Cite
THỊNH, Đặng Thái; HAI, Duong Trong.
A HYBRID FRAMEWORK FOR ENHANCING CORRELATION TO SOLVE COLD-START PROBLEM IN RECOMMENDER SYSTEMS.
JBIS, [S.l.], dec. 2014.
Available at: <http://jbis.ueh.edu.vn/index.php/TSTHQL/article/view/41>. Date accessed: 26 apr. 2024.
Issue
Section
Bài viết
Keywords
information filtering; collaborative filtering; demographic filtering; recommendation; personalization
