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International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-7 Issue-29 March-2017
Full-Text PDF
DOI:10.19101/IJACR.2017.728008
Paper Title : An improvement on recommender systems by exploring more relationships
Author Name : Hoang Lam Le, Quoc Cuong Nguyen and Minh Tri Nguyen
Abstract :

Recommender systems are systems that can filter a great number of pieces of data and suggest mostly similar interested items of the user’s preference. A variety of approaches have been proposed to perform recommendation, including content-based, collaborative filtering and association-based, etc. A potential problem existing in a recommender system is cold start [1]; simply defined that a system cannot draw any inference for users. In this paper, we deal with one of cold start problems by proposing a hybrid approach which combines two distinct features to solve the problem. While a user is related to other users in product purchase behaviors or preference, an item is connected to different items by its inside information. Our recommender system utilizes both these relations instead of each individual one to ameliorate the quality of output suggestion. This procedure will be revealed and discussed through this paper.

Keywords : Cold start, Recommendation, Recommender, Collaborative filtering, Content-based, Hybrid approach.
Cite this article : Hoang Lam Le, Quoc Cuong Nguyen and Minh Tri Nguyen , " An improvement on recommender systems by exploring more relationships " , International Journal of Advanced Computer Research (IJACR), Volume-7, Issue-29, March-2017 ,pp.42-51.DOI:10.19101/IJACR.2017.728008
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