Research on Collaborative Recommendation Method Based on Multiple Data Sources of Social Network
As an effective recommendation method，collaborative filtering typically has the data sparsity and cold-start problems. It was proposed that using multiple data sources of social network to overcome the above problems. First of a11，both the rating similarity and the social trust between users were co...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2015-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015113/ |
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| Summary: | As an effective recommendation method，collaborative filtering typically has the data sparsity and cold-start problems. It was proposed that using multiple data sources of social network to overcome the above problems. First of a11，both the rating similarity and the social trust between users were considered to resolve the data sparsity problem. Then a simple and effective trust reasoning method was proposed to identify the implicit trust relationship between users. In order to solve the cold-start problem，information of the category of items and domain experts was used for joint recommendation. Experimental results show that the proposed algorithm has significantly better precision and reca11 than existing methods. |
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| ISSN: | 1000-0801 |