Research on cross-domain recommendation algorithm based on quadratic collaborative filtering
Jiang Chun, Tang Lin, Hong Shi · 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · 2022
Among the existing recommendation algorithms, the single domain recommendation algorithm has serious defects, and the cross-domain recommendation algorithm becomes a new development direction. To solve the problem of data missing in collaborative filtering algorithm, a cross-domain recommendation algorithm for quadratic collaborative filtering is proposed. The accuracy of the recommendation system can be improved by predicting the missing data with two similarity matrices. Through empirical study, experimental results show that the quadratic collaborative filtering cross-domain recommendation algorithm can achieve the purpose of cross-domain recommendation, and the performance comparison with the collaborative filtering recommendation algorithm shows that the quadratic collaborative filtering cross-domain recommendation algorithm can improve the accuracy of prediction.