The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem
Abstract
Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. Cold start problem is one of the prevailing issues in recommendation system where the system fails to render recommendation. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of user gender is less explored when compared with other information like age, profession, region, etc. In this chapter, genetic algorithm influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state-of-art approaches.
Related Content
Shailendra Aote, Mukesh M. Raghuwanshi.
© 2021.
34 pages.
|
Anjana Mishra, Bighnaraj Naik, Suresh Kumar Srichandan.
© 2021.
15 pages.
|
Thendral Puyalnithi, Madhuviswanatham Vankadara.
© 2021.
15 pages.
|
Geng Zhang, Xiansheng Gong, Xirui Chen.
© 2021.
13 pages.
|
Jhuma Ray, Siddhartha Bhattacharyya, N. Bhupendro Singh.
© 2021.
19 pages.
|
Pijush Samui, Viswanathan R., Jagan J., Pradeep U. Kurup.
© 2021.
18 pages.
|
Ravinesh C. Deo, Sujan Ghimire, Nathan J. Downs, Nawin Raj.
© 2021.
32 pages.
|
|
|