Cross-Domain Recommendation Algorithm Based on Latent Factor Model - Sheng Gao; Siting Ren; Jun Guo (Dianxin kexue)
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.