DCFRS:A Distributed Collaborative Filtering Recommender System based on Cloud Computing for Mobile Commerce
Yaming Zhang, Haiou Liu · 2013
As the state-of-the-art method in recommender systems, collaborative filtering (CF) has proved to be one of the most successful algorithms in various applications. However, traditional centralized collaborative filtering recommender systems (CFRS) suffers from sparse data problem and a lack of scalability as their calculation complexity increases quickly both in time and space when the number of the records in the user database increases. As a result, distributed collaborative filtering (DCF) is attracting increasing attention as an alternative implementation scheme. In this paper, we proposed a distributed collaborative filtering recommender system (DCFRS) for mobile commerce based on cloud computing because of its advantage of scalability as an alternative architecture. The experimental results demonstrate that the proposed algorithm improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of the proposed cloud-based DCF scenario.