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Graph-based collaborative ranking
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AI
DOI：10.1016/j.eswa.2016.09.013.png)
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摘要
En 中文
Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users' similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users' priorities, and second, they can't be used when the only available data is a set of ranking instead of rating values. In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users' priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
Collaborative ranking
Pairwise preferences
Graph modelling
Recommendation systems
Personalized PageRank
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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引用论文
Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings个性化内容推荐与用户满意度: 理论综述与实证研究
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