scholarly journals An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Vala Ali Rohani ◽  
Zarinah Mohd Kasirun ◽  
Sameer Kumar ◽  
Shahaboddin Shamshirband
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Prediction Accuracy ◽  
Cold Start ◽  
Information Spaces ◽  
Novel Approach ◽  
Significant Performance ◽  
Academic Social Network ◽  
Recommender Algorithm ◽  
Cold Start Problem

Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs) have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN), the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN) for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.

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.

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

2021 ◽  
Vol 17 (3) ◽  
pp. 30-49
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L. D.
Keyword(s):  
World Wide ◽  
Cold Start ◽  
The Other ◽  
The World ◽  
Tremendous Amount ◽  
Real World Datasets ◽  
Recommender Algorithm ◽  
The Right ◽  
Cold Start Problem ◽  
Better Than

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.

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.

P2PCF: A collaborative filtering based recommender system for peer to peer social networks

2021 ◽  
pp. 1-19
Author(s):  
Lyes Badis ◽  
Mourad Amad ◽  
Djamil Aïssani ◽  
Sofiane Abbar
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Peer To Peer ◽  
Public Concerns ◽  
Cold Start Problem ◽  
Filtering Approach

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage.

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