Content Based Filtering And Collaborative Filtering: A Comparative Study
DOI:
https://doi.org/10.53555/jaz.v45iS4.4158Keywords:
Machine-learning, Recommendation system, Collaborative Filtering, Content-Based Filtering, hybrid FilteringAbstract
Collecting data from users is a frequent practice for websites to improve various aspects of their products and services, such as performance, usability, and security. Monitoring user activity on websites helps to comprehend visitor behavior and assess the impact of the site. Numerous applications involve the collection of user data by websites, enabling the prediction of user preferences. This, in turn, facilitates personalized content recommendations. Recommender systems serve as a mechanism to propose analogous items and concepts tailored to an individual's unique mindset. Fundamentally, there are two categories of recommender systems: Collaborative Filtering and Content-Based Filtering. This paper provides a comparative study of collaborative filtering and content-based filtering.
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Hameed, M. A., Al Jadaan, O., & Ramachandram, S. (2012). Collaborative filtering based recommendation system: A survey. International Journal on Computer Science and Engineering, 4(5), 859.
Das, D., Sahoo, L., & Datta, S. (2017). A survey on recommendation system. International Journal of Computer Applications, 160(7).
Thorat, P. B., Goudar, R. M., & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 31-36.
Amin, S. A., Philips, J., & Tabrizi, N. (2019). Current trends in collaborative filtering recommendation systems. In Services–SERVICES 2019: 15th World Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings 15 (pp. 46-60). Springer International Publishing.
Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web: methods and strategies of web personalization (pp. 291-324). Berlin, Heidelberg: Springer Berlin Heidelberg.
Thorat, P. B., Goudar, R. M., & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 31-36.
Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018, April). A hybrid approach using collaborative filtering and content based filtering for recommender system. In Journal of Physics: Conference Series (Vol. 1000, p. 012101). IOP Publishing.
https://www.itransition.com/machine-learning/recommendation-systems
https://medium.com/@toprak.mhmt/collaborative-filtering-3ceb89080ade
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Copyright (c) 2024 Ms. Tejashri Sharad Phalle, Prof. Shivendu Bhushan
This work is licensed under a Creative Commons Attribution 4.0 International License.
