Collaborative Filtering Recommender System Using Genetic Algorithm. Bhusal, M. & Shakya, A. In Proceedings of the 6th IOE Graduate Conference, (IOEGC-2019), Kathmandu, Nepal, pages 133–139, 2019. ![Collaborative Filtering Recommender System Using Genetic Algorithm [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Information overload in the internet has caused users to rely on Recommender Systems for information filtering. However the quality of the recommendations has always been a challenge. Many techniques have been developed for the improvement of quality and performance of recommender systems. Collaborative Filtering is the most used approach in recommender systems. This paper presents a technique that combines the idea of collaborative filtering with genetic algorithm. In this approach Genetic Algorithm is used to find the optimal similarity value between users. Each individual in the population represents the similarity matrix between the users. Thus, the proposed system does not directly compute any similarity metric but learns the similarity among users which helps to minimize the effect of sparsity and cold start problems common in collaborative filtering. A series of experiments have been conducted that demonstrate the effectiveness of the approach in terms of the quality of recommendations.
@inproceedings{bhusal2019collaborative,
abstract = {Information overload in the internet has caused users to rely on Recommender Systems for information filtering. However the quality of the recommendations has always been a challenge. Many techniques have been developed for the improvement of quality and performance of recommender systems. Collaborative Filtering is the most used approach in recommender systems. This paper presents a technique that combines the idea of collaborative filtering with genetic algorithm. In this approach Genetic Algorithm is used to find the optimal similarity value between users. Each individual in the population represents the similarity matrix between the users. Thus, the proposed system does not directly compute any similarity metric but learns the similarity among users which helps to minimize the effect of sparsity and cold start problems common in collaborative filtering. A series of experiments have been conducted that demonstrate the effectiveness of the approach in terms of the quality of recommendations.},
added-at = {2023-03-07T12:13:40.000+0100},
author = {Bhusal, Manoj and Shakya, Aman},
biburl = {https://www.bibsonomy.org/bibtex/29f6dc3414f7a0a8018dc4f44855fc2fd/amanshakya},
booktitle = {Proceedings of the 6th IOE Graduate Conference, (IOEGC-2019), Kathmandu, Nepal},
eventdate = {24-25 May, 2019},
eventtitle = {6th IOE Graduate Conference, (IOEGC-2019)},
interhash = {ea6d44df4cf6c6620175900f0f143ac2},
intrahash = {9f6dc3414f7a0a8018dc4f44855fc2fd},
keywords = {imported myown},
pages = {133--139},
timestamp = {2023-03-17T08:56:11.000+0100},
title = {Collaborative Filtering Recommender System Using Genetic Algorithm},
url = {http://conference.ioe.edu.np/publications/ioegc2019-summer/IOEGC-2019-Summer-018.pdf},
venue = {Kathmandu, Nepal},
year = 2019
}
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