基于多关系分析的社会推荐

Jian Chen, Guanliang Chen, H. Zhang, Jin Huang, Gansen Zhao
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引用次数: 10

摘要

社会推荐方法往往只考虑社会网络中的一种关系,仍然面临着数据稀疏和冷启动用户的问题。本文提出了一种新的基于多关系分析的推荐方法:首先利用最优线性回归分析对不同的关系网络进行组合,然后在最优网络组合的基础上,提出了一种与多关系社会网络相结合的推荐算法。在Epinions数据集上的实验结果表明,与现有算法相比,该算法可以有效缓解数据稀疏性和冷启动问题,并取得更好的性能。
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Social Recommendation Based on Multi-relational Analysis
Social recommendation methods, often taking only one kind of relationship in social network into consideration, still faces the data sparsity and cold-start user problems. This paper presents a novel recommendation method based on multi-relational analysis: first, combine different relation networks by applying optimal linear regression analysis, and then, based on the optimal network combination, put forward a recommendation algorithm combined with multi-relational social network. The experimental results on Epinions dataset indicate that, compared with existing algorithms, can effectively alleviate data sparsity as well as cold-start issues, and achieve better performance.
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