Cross-Domain Recommendation Algorithm Based on Latent Factor Model
In the internet environment，the combining of multi-source heterogeneous information objects in different areas makes users face information selection dilemma problem in big data environment.It has been very difficulty for traditional information recommendation algorithms to adapt to the interdiscipl...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2015-07-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015188/ |
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| author | Sheng Gao Siting Ren Jun Guo |
| author_facet | Sheng Gao Siting Ren Jun Guo |
| author_sort | Sheng Gao |
| collection | DOAJ |
| description | In the internet environment，the combining of multi-source heterogeneous information objects in different areas makes users face information selection dilemma problem in big data environment.It has been very difficulty for traditional information recommendation algorithms to adapt to the interdisciplinary information recommendation service.The evaluation model from a user clustering set to an information object clustering set has common characteristics of cross-domain and personality characteristics of single domain.By analyzing the evaluation data from users to information objects in different areas，these characteristics were extracted based on latent factor model.Then by transmitting and sharing the common characteristics of cross-domain，the data sparseness problem of target field was alleviated，which could improve the accuracy of cross-domain information recommendation. |
| format | Article |
| id | doaj-art-3711461d4c7c4204933ba3564a4c7a11 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2015-07-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-3711461d4c7c4204933ba3564a4c7a112025-01-15T03:17:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012015-07-0131757959614704Cross-Domain Recommendation Algorithm Based on Latent Factor ModelSheng GaoSiting RenJun GuoIn the internet environment，the combining of multi-source heterogeneous information objects in different areas makes users face information selection dilemma problem in big data environment.It has been very difficulty for traditional information recommendation algorithms to adapt to the interdisciplinary information recommendation service.The evaluation model from a user clustering set to an information object clustering set has common characteristics of cross-domain and personality characteristics of single domain.By analyzing the evaluation data from users to information objects in different areas，these characteristics were extracted based on latent factor model.Then by transmitting and sharing the common characteristics of cross-domain，the data sparseness problem of target field was alleviated，which could improve the accuracy of cross-domain information recommendation.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015188/cross-domain recommendationlatent factor modeluser rating pattern |
| spellingShingle | Sheng Gao Siting Ren Jun Guo Cross-Domain Recommendation Algorithm Based on Latent Factor Model Dianxin kexue cross-domain recommendation latent factor model user rating pattern |
| title | Cross-Domain Recommendation Algorithm Based on Latent Factor Model |
| title_full | Cross-Domain Recommendation Algorithm Based on Latent Factor Model |
| title_fullStr | Cross-Domain Recommendation Algorithm Based on Latent Factor Model |
| title_full_unstemmed | Cross-Domain Recommendation Algorithm Based on Latent Factor Model |
| title_short | Cross-Domain Recommendation Algorithm Based on Latent Factor Model |
| title_sort | cross domain recommendation algorithm based on latent factor model |
| topic | cross-domain recommendation latent factor model user rating pattern |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015188/ |
| work_keys_str_mv | AT shenggao crossdomainrecommendationalgorithmbasedonlatentfactormodel AT sitingren crossdomainrecommendationalgorithmbasedonlatentfactormodel AT junguo crossdomainrecommendationalgorithmbasedonlatentfactormodel |