TY  - EJOU
AU  - Liang, Zhaomin 
AU  - Liang, Tingting 

TI  - Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network
T2  - Computer Systems Science and Engineering

PY  - 2023
VL  - 47
IS  - 1
SN  - 

AB  - The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry. Most book recommendation systems also use this algorithm. However, the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well. This algorithm only uses the shallow feature design of the interaction between readers and books, so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books, leading to a decline in recommendation performance. Given the above problems, this study uses deep learning technology to model readers’ book borrowing probability. It builds a recommendation system model through the multi-layer neural network and inputs the features extracted from readers and books into the network, and then profoundly integrates the features of readers and books through the multi-layer neural network. The hidden deep interaction between readers and books is explored accordingly. Thus, the quality of book recommendation performance will be significantly improved. In the experiment, the evaluation indexes of HR@10, MRR, and NDCG of the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm, which verifies the effectiveness of the model in the book recommendation.
KW  - Book recommendation; deep learning; neural network; multi-feature fusion personalized prediction

DO  - 10.32604/csse.2023.037124