Book Recommendation Model Based on Wide and Deep Model

Abstract

[Purpose] Personalized recommendation is one of the hottest research areas in recent years Recommendation systems developed by Google, Amazon, Alibaba and other companies have brought them huge benefits, which are recommendations based on big data analysis. However,For data sets with large data sparseness, traditional recommendation algorithms for historical records cannot obtain satisfactory recommendation results, and traditional recommendation algorithms often cannot discover the potential interests of users. In this paper, we managed to extend the personalized recommendation system to the University Library Lending system [Methodology]Firstly, to meet the challenge of data sparsity, we collected the information of readers and books in the borrowing records of Qinghai University Library in recent 20 years. Secondly, through the analysis and research of the Wide and Deep model, the recommendation model is obtained by joint training of LR (Logistic Regression) and DNN (Deep Neural Network) networks. Moreover, we improved the double-label of the Wide and Deep model into multiple labels and got the final model after extensive training. [Findings]The experimental results show that the accuracy of our book recommendation model is significantly better than traditional recommendation algorithms and hybrid recommendation algorithms. [Originality]Firstly we set up a large Qinghai University book data set for training and testing and verification.Secondly, completed the model migration and improvement of W & D. Through a large number of experiments for comparative research, it is concluded that the improved W & D model is suitable for book recommendation systems.The value of the AUC index of the traditional collaborative filtering model is the lowest. The AUC value of the weighted bipartite graph model is greater than the collaborative filtering model. The AUC value of the hybrid model is basically the same as that of the weighted bipartite graph model. The Wide & Deep model has the highest AUC value. Reached 0.75. Therefore, the Wide & Deep model is suitable for a book personalized recommendation system with sparse characteristics of big data.