A Standard Bibliography Recommended Method Based on Topic Model and Fusion of Multi-feature

This paper proposed a recommended method of standard bibliography based on topic model which fused multi-feature. Firstly, the LDA topic model was used to analyze the standard resource which user concerned, then the user attention model was created by combined with the user's information; Secondly, by analyze the feature of standard bibliography documents in attribute, classification and association relationship, the semi-supervised graph clustering algorithm was proposed to realize the construction of the standard bibliography topic model; Finally, the standard bibliography model and user attention model were used to complete the calculation of similarity, by using Top-N algorithm, the highest standard resource was recommend to users. Some experiments based on the Standard Library have been made, the results shown that the F value in the method which proposed in this paper is about 9% higher than the recommendation algorithm based on vector space model, and about 5% higher than the recommended method based on implicit topic model.