With the rapid development of the Internet, the growing amount of information has gone beyond the processing capacity of people. People often feel overwhelmed in the sea of information, but cannot find the resources they need. The problem of overloading information has become more and more serious. Although search engines such as Google and Baidu can help user filter useless information through key words, the results returned are still huge. Simultaneously, users do not have explicit objective at most of the time. In order to provide users with satisfactory service and information they actually need, recommendation system emerged. Collaborative filtering is one of one of the most important recommendation technical, which makes prediction with the similarity between user profiles. The development of tagging system service and communication make the recommendation technical based on the simple relation between users and items perform not precise enough. Considering the tags semantic and potential relationship between tags and users or items are necessary. In social tagging systems, tags exist as a highly abstract description of users preferences for items. Thus, tags could be used to extract users and resources features. However, if the data in the form of three-dimension (user, tag, resources) are flattening into three two-dimension spaces, the implicit information between users, tags and resources will be lost. In summary, based on the existing recommendation technologies, this paper has proposed a bilinear model based recommendations method. To capture the lost associated information, the proposed method fit the process of users selecting a product in its training phase. Then, this paper uses the method based on Bayesian conditional probability to verify the theoretical basis of this model. Finally, the paper validates the proposed bilinear model based tags recommendation algorithm with experimental analysis, and compares it with other major recommendation algorithms. The results of the experiment show that the proposed bilinear model based recommendation algorithm has certain improvements in recommendation precision.
修改评论