基于情境和主体特征融入性的多维度个性化推荐模型研究

个性化推荐准确率的高低是互联网应用成功与否的关键因素，针对传统推荐模型的不足，提出一种基于情境和主体特征融入性的多维度个性化推荐模型，该模型能够充分利用地域文化背景、领域主题情景、主体特征等信息，避免了传统算法把用户整体作为单个向量的弊端，克服了数据稀疏性等问题。实验结果表明，该模型的推荐质量比传统的协同推荐模型高，更有针对性地向用户推荐他们感兴趣的项目。

Research on a multidimensional personalized recommendation model based on a situation and characteristics of the users

The accuracy of personalized recommendation was the key factor of Internet application to success.Because of the deficiency of the traditional recommend model,a multidimensional personalized recommendation model based on a special situation and main characteristics of the users was proposed.This model could make full use of regional culture background,field scene,characteristics of the users and so on,avoided the disadvantages of traditional algorithm,put the user's overall characteristics as a single vector,and overcomed the problem of sparse data.The experimental results show that the quality of this recommendation model is better than traditional collaborative recommend model with the more specific items match user interests.