Improvised Collaborative Filtering for Recommendation System
Shefali Gupta1, Meenu Dave2
1Shefali Gupta*, Ph.D. Scholar, Department of Information Technology, Jagannath University Jaipur, Rajasthan, India.
2Prof. (Dr.) Meenu Dave, Ph.D., Department of Computer Science, Jagannath University Jaipur, Rajasthan, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 28, 2020. | Manuscript published on April 10, 2020. | PP: 361-364 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3513049620/2020©BEIESP | DOI: 10.35940/ijitee.F3513.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Collaborative filtering (CF) is one of the most important techniques of recommendation system and has been utilized by many e-commerce businesses to provide recommendation to its users. This paper sheds light on CF and its methods. This paper demonstrates a practical algorithm by leveraging data on user ratings for mobile phone devices and then provides recommendations to the target user based on the ratings given by similar users. It also elaborates an algorithm of CF that overcomes some of the common limitations faced by other algorithms. To explain the methodology of collaborative filtering this research paper looks at mobile phone data, especially the mapping of users (buyers) and the rating they provide for mobile phones they purchase. The model first evaluate multiple collaborative filtering techniques (variations of user based and item based filtering) by use of ROC curve and then provide recommendation to the user based on the best identified technique. Collaborative filtering is best utilized where the information on “users” and/or item is limited. For example, you can imagine the hotel booking website that provides recommendation to the website visitor, even though the user has never visited the website before (first time user). In such a situation as the information about user is limited the website algorithms are still able to utilize collaborative filtering methodology to provide recommendations.
Keywords: Recommendation System, Collaborative Filtering.
Scope of the Article: Collaborative Applications