Accurate Item Recommendation Algorithm of itemrank based on tag and context information

The traditional itemrank recommendation algorithm only uses the two-dimensional relationship between user and item to achieve recommendation, without considering the important information (such as label information and context information) that plays an important role in the association between user and item, so the accuracy of recommendation needs to be improved. Therefore, in this paper, tag information and context information are integrated into the link relationship between user and item, and a user-context-item tag association graph is constructed. An itemrank recommendation algorithm is proposed by integrating tag and context information. Firstly, ap-ml-rbf-relm model is used to determine user labels, and then deep neural network is used to determine the relationship between tags. Finally, item and label are calculated The association weight between the signatures is used to implement the recommendation service for the target users. Experimental results on public datasets show that the proposed algorithm is better than the traditional recommendation algorithm in recommendation accuracy.

Automated summary

This paper proposes a recommendation algorithm that integrates tag information and context information into the link relationship between users and items, achieving better recommendation accuracy compared to traditional recommendation algorithms.