Information Gain Clustering through Roulette Wheel Genetic Algorithm (IGCRWGA): A Novel Heuristic Approach for Personalisation of Cold Start Problem

Real World Datasets ◽

Gender Based ◽

Recommender Algorithm ◽

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.

A Heuristic Approach for New-Item Cold Start Problem in Recommendation of Micro Open Education Resources

Intelligent Tutoring Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-319-91464-0_21 ◽

2018 ◽

pp. 212-222 ◽

Cited By ~ 2

Author(s):

Geng Sun ◽

Tingru Cui ◽

Dongming Xu ◽

Jun Shen ◽

Shiping Chen

Keyword(s):

Cold Start ◽

Heuristic Approach ◽

Open Education ◽

Education Resources ◽

Open Education Resources ◽

New Item ◽

Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems - Advances in Computational Intelligence and Robotics ◽

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

10.4018/978-1-7998-3222-5.ch010 ◽

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 ◽

Real World Datasets ◽

Gender Based ◽

Recommender Algorithm ◽

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

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms ◽

10.4018/978-1-7998-8048-6.ch042 ◽

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 ◽

Real World Datasets ◽

Gender Based ◽

Recommender Algorithm ◽

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.

IGCRGA: A Novel Heuristic Approach for Personalization of Cold Start Problem

2011 Fifth Asia Modelling Symposium ◽

10.1109/ams.2011.20 ◽

2011 ◽

Author(s):

Mohammed Abdul Hameed ◽

S. Ramachandram ◽

Omar Al Jadaan

Keyword(s):

Cold Start ◽

Heuristic Approach ◽

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

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2020040104 ◽

2020 ◽

Vol 11 (2) ◽

pp. 62-79

Author(s):

Sharon Moses J. ◽

Dhinesh Babu L.D.

Keyword(s):

Genetic Algorithm ◽

Collaborative Filtering ◽

Recommendation System ◽

Information Source ◽

Cold Start ◽

Filtering Algorithm ◽

Real World Datasets ◽

The Impact ◽

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.

A genetic algorithm approach to recommender system cold start problem.

10.32920/ryerson.14651604 ◽

2021 ◽

Author(s):

Sanjeevan Sivapalan

Keyword(s):

Genetic Algorithm ◽

Recommender Systems ◽

User Experience ◽

Recommender System ◽

Evaluation Method ◽

New Products ◽

Cold Start ◽

Genetic Algorithm Approach ◽

Cold Start Problem ◽

Novel Algorithm

Recommender systems (RS) are ubiquitous and used in many systems to augment user experience to improve usability and they achieve this by helping users discover new products to consume. They, however, suffer from cold-start problem which occurs when there is not enough information to generate recommendations to a user. Cold-start occurs when a new user enters the system that we don’t know about. We have proposed a novel algorithm to make recommendations to new users by recommending outside of their preferences. We also propose a genetic algorithm based solution to make recommendations when we lack information about user and a transitive algorithm to form neighbourhood. Altogether, we developed three algorithms and tested them using they MovieLens dataset. We have found that all of our algorithms performed well during our testing using the offline-evaluation method.