Genre Familiarity Correlation-Based Recommender Algorithm for New User Cold Start Problem

Sharon Moses J. (6f18f20b-E30f-4382-Bfc1-3c1efb2107b9 and Dhinesh Babu L. D. (58c0465d-D35d-4fbd-9f7d-5ed7d33c5b50
Additional contact information
Sharon Moses J. (6f18f20b-E30f-4382-Bfc1-3c1efb2107b9: Fanlytiks, India
Dhinesh Babu L. D. (58c0465d-D35d-4fbd-9f7d-5ed7d33c5b50: VIT University, India

International Journal of Intelligent Information Technologies (IJIIT), 2021, vol. 17, issue 3, 1-20

Abstract: The advancement of web services paved the way to the accumulation of a tremendous amount of information into the world wide web. The huge pile of information makes it hard for the user to get the required information at the right time. Therefore, to get the right item, recommender systems are emphasized. Recommender algorithms generally act on the user information to render recommendations. In this scenario, when a new user enters the system, it fails in rendering recommendation due to unavailability of user information, resulting in a new user problem. So, in this paper, a movie recommender algorithm is constructed to address the prevailing new user cold start problem by utilizing only movie genres. Unlike other techniques, in the proposed work, familiarity of each movie genre is considered to compute the genre significance value. Based on genre significance value, genre similarity is correlated to render recommendations to a new user. The evaluation of the proposed recommender algorithm on real-world datasets shows that the algorithm performs better than the other similar approaches.

Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 018/IJIIT.2021070103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:17:y:2021:i:3:p:1-20

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().