Comparison Between Collaborative Filtering and Content-Based Filtering
DOI:
https://doi.org/10.54097/hset.v16i.2627Keywords:
Recommendation algorithms, collaborative filtering, content-based filtering, information filtering.Abstract
With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by describing the steps of these two algorithms gradually and analyzing their processes step by step, we can accurately analyze and summarize their advantages and disadvantages respectively. And on this basis, the respective areas which they are good at are mentioned. Moreover, this paper points out the shortcomings and limitations that still exist at present, and the direction for further improvement in the future. Finally, at the end of the paper, there are some overall comparation and summation about the two algorithms. And the hot research points of them in the future are discussed.
Downloads
References
Zhou ZhiMin. Research and application of friend recommendation algorithm in social network. Kunming: Kunming University of Science and Technology, 2016.
Li Jing. Research on recommendation strategy of academic friends based on paper cooperation network. Dalian: University of Technology, 2014.
Yu Yan. Research on friend recommendation algorithm in social network. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014.
Fei Zhongyou. Research and application of random walk technology. Chengdu: Southwest Jiaotong University, 2014.
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992, 35(12), 61-70.
Kim, S. C., Sung, K. J., Park, C. S., & Kim, S. K. Improvement of collaborative filtering using rating normalization. Multimedia tools and applications, 2016, 75(9): 4957-4968.
Wu Tao. Research and application of recommendation algorithm in recommendation system. Beijing: Beijing Jiaotong University, 2019.
Zhao Fengyue. Research on collaborative filtering and content-based hybrid recommendation algorithm. Tianjin: Tianjin University of Finance and Economics, 2016.
Reng Lei. Research on Key technologies of recommender system. Shanghai: East China Normal University, 2012.
Liu Shuai. Research on correlation measurement of high dimensional sparse data. Beijing: Capital University of Economics and Business, 2014.
Lops P, Gemmis M, Semeraro G. Content-based recommender systems: State of the art and trends. Recommender systems handbook, 2011: 73-105.
Barbu, T. Content-based image retrieval using gabor filtering. IEEE In 2009 20th International Workshop on Database and Expert Systems Application, 2009.
Feng Shan. Research on Friend recommendation algorithms in social networks. Beijing University of Posts and Telecommunications, 2015.
Tkalčič, M., Burnik, U., & Košir, A. Using affective parameters in a content-based recommender system for images. User Modeling and User-Adapted Interaction, 2010, 20(4): 279-311.
Nie Shuaihua. Design & Implementation of intelligent dating website based on content recommendation or collaborative filtering recommendation algorithm. Wuhan: Central China Normal University, 2016.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, 1994.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
