Mr. Gang Zheng Profile

Gang Zheng

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Proceedings Article | 8 June 2023 Paper

Research on personalized recommendation algorithms for the web with knowledge representation

Na Chen, Ying Li, Gang Zheng

Proceedings Volume 12707, 127074G (2023) https://doi.org/10.1117/12.2681370

KEYWORDS: Tunable filters, Head, Feature extraction, Machine learning, Education and training, Data modeling, Semantics, Systems modeling, Model-based design, Genetic algorithms

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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.

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