Research and Design of Hybrid Collaborative Filtering Algorithm Scalability Reform Based on Genetic Algorithm Optimization

2016 ◽  
Author(s):  
Liu Shou-Qiang ◽  
Qi Ming ◽  
Xu Qing-Zhen
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Hybrid Collaborative Filtering ◽  
Research And Design ◽  
Algorithm Scalability

Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem

2021 ◽  
pp. 1492-1512
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L.D.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

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. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. 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 the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a 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 the 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.

Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem

2020 ◽  
pp. 195-216
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L. D. ◽  
Santhoshkumar Srinivasan ◽  
Nirmala M.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

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.

Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem

2021 ◽  
pp. 829-850
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L. D. ◽  
Santhoshkumar Srinivasan ◽  
Nirmala M.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

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.

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