Research on personalized recommendation algorithms for the web with knowledge representation

Na Chen; Ying Li; Gang Zheng

doi:10.1117/12.2681370

8 June 2023 Research on personalized recommendation algorithms for the web with knowledge representation

Na Chen, Ying Li, Gang Zheng

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127074G (2023) https://doi.org/10.1117/12.2681370
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

With the rapid development of information technology and the increasing scale of the Internet, a huge amount of data and information has been generated, and people face a huge challenge to get the information they need from it. In order to solve these challenges, personalized recommendation technology has emerged, which can actively recommend items of potential interest to users. The most mainstream personalized recommendation technology is collaborative filtering, which has been applied in various fields and achieved good results. However, its recommendation performance tends to drop sharply when facing data sparsity and cold-start problems. Currently, knowledge representation techniques have attracted wide attention from academia and industry, and have been applied to recommender systems and other fields, and have made important breakthroughs. To solve the problem of data sparsity and improve recommendation accuracy, this paper introduces knowledge representation into neural collaborative filtering model and proposes a neural collaborative filtering model assisted by knowledge graph embedding. By alternating the training of the knowledge representation module of the recommendation module, the knowledge representation module assists the training of the recommendation module, which effectively improves the rating prediction effect. Through experiments, it is shown that the model not only improves 9.46% and 10.18% in MAE and RMSE respectively over the UserCF method, but also effectively alleviates the data sparsity problem.

Citation Download Citation

Na Chen, Ying Li, and Gang Zheng "Research on personalized recommendation algorithms for the web with knowledge representation", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127074G (8 June 2023); https://doi.org/10.1117/12.2681370

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KEYWORDS

Tunable filters

Head

Feature extraction

Education and training

Machine learning

Data modeling

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