Journal
KNOWLEDGE-BASED SYSTEMS
Volume 177, Issue -, Pages 11-21Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2019.03.032
Keywords
Recommender systems; Multi-objective optimization; MapReduce; Accuracy; Diversity
Categories
Funding
- National Natural Science Foundation of China [71702164]
Ask authors/readers for more resources
Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining. For traditional CF-based recommender systems, the accuracy of recommendation results can be guaranteed while the diversity will be lost. An ideal recommender system should be built with both accurate and diverse performance. Faced with accuracy-diversity dilemma, we propose a novel recommendation method based on MapReduce framework. In MapReduce framework, a block computational technique is used to shorten the operational time. And an improved collaborative filtering model is refined with a novel similarity computational process which considers many factors. By translating the procedure of generating personalized recommendation results into a multi-objective optimization problem, the multiple conflicts between accuracy and diversity are well handled. The experimental results demonstrate that our method outperforms other state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available