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Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction
- 1 Glasgow College, University of Electronic Science and Technology of China, Sichuan, 611700, China
- 2 College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China.
- 3 Ningbo Binhai International Cooperative School, Ningbo, 315800, China
- 4 Southland International High School Qingdao Campus, Qingdao, 266555, China
- 5 Guangdong Experimental High School, Guangzhou, 510000, China
* Author to whom correspondence should be addressed.
Abstract
With the development of recommendation systems, large amount of information collected from e-commerce could help customers to find the potential interesting products. Collaborative filtering and content-based recommendation systems are two common recommendation systems. While collaborative filtering has the problem of cold-start, content-based recommendation system could not explore the potential interests of users. Hybrid system combining these two techniques could achieve better results. This paper applies hybrid recommendation methods to the Amazon food reviews and evaluate the results in the aspects of precision, recall, diversity and novelty. It is found that the weighted hybrid recommendation system combing 0.95 weight of collaborative filtering and 0.05 content-based recommendation system achieves a good precision and diversity.
Keywords
recommendation systems, collaborative filtering, hybrid system.
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Cite this article
Zhang,S.;Liu,K.;Yu,Z.;Feng,B.;Ou,Z. (2023). Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction. Applied and Computational Engineering,2,149-161.
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