Title: A Concurrent Recommender System Based on Social Network
Abstract: Recommender systems are widely used to improve sales for online retailers. Due to the large number of users and items, reducing computation time and space requirement for a recommender system has become challenging. In this paper, we propose a simple and effective recommender system based on social network of users. We implemented our proposed recommender system using a hashing technique to take advantage of parallel access to the items rated highly by users. To validate our recommendation method, we performed experiments on a real-world data set. The experimental results demonstrate the effectiveness of the proposed recommender system.
Publication Year: 2018
Publication Date: 2018-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 1
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