An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniques

Autor:	Radhya Sahal, Sahar Selim, Abeer ElKorany
Rok vydání:	2014
Předmět:	Set (abstract data type) Process (engineering) Computer science Scalability Collaborative filtering Data mining Recommender system computer.software_genre Linear formula computer Poor quality
Zdroj:	International Journal of Computer Science and Information Technology. 6:51-66
ISSN:	0975-3826 0975-4660
DOI:	10.5121/ijcsit.2014.6204
Popis:	Recommender systems provide useful recommendations to a set of users for items or products that may be of interest to them. Several techniques have been proposed for recommendation such as collaborative, content-based, knowledge-based, and demographic techniques. Each of these techniques suffers from scalability, data sparsity, and cold-start problems when applied individually resulting in poor quality recommendations. This paper proposes an adaptive hybrid recommender system that combines multiple techniques together to enhance the overall recommendation process. Collaborative filtering and demographic techniques are combined in a weighted linear formula. Experiment results using movieLen dataset show that this adaptable hybrid framework is able to outperform the weaknesses shown by traditional recommendation techniques.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d4c974b9ff6ddc8e325c59ef36d08e04 https://doi.org/10.5121/ijcsit.2014.6204 Zobrazit plný text záznamu