Version 1
: Received: 28 November 2022 / Approved: 29 November 2022 / Online: 29 November 2022 (04:19:09 CET)
How to cite:
webster, E.; Hill, J.; Shimada, T.; Smyth, B. GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks. Preprints2022, 2022110534. https://doi.org/10.20944/preprints202211.0534.v1
webster, E.; Hill, J.; Shimada, T.; Smyth, B. GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks. Preprints 2022, 2022110534. https://doi.org/10.20944/preprints202211.0534.v1
webster, E.; Hill, J.; Shimada, T.; Smyth, B. GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks. Preprints2022, 2022110534. https://doi.org/10.20944/preprints202211.0534.v1
APA Style
webster, E., Hill, J., Shimada, T., & Smyth, B. (2022). GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks. Preprints. https://doi.org/10.20944/preprints202211.0534.v1
Chicago/Turabian Style
webster, E., Tyson Shimada and Braedon Smyth. 2022 "GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks" Preprints. https://doi.org/10.20944/preprints202211.0534.v1
Abstract
Recommender systems as an effective information filtering system can be used to obtain information through the user's explicit or implicit behavior. On the one hand finding items that may be of interest to the user. On the other hand, the recommendation facilitates the interaction between the user and the item to increase the revenue. Recommender systems have been widely used in various fields, such as e-commerce, travel recommendation, online books and movies, social networks, etc, which can satisfy the intrinsic implicit needs of users through personalized services. In recent years, the development of deep learning has further improved the performance of recommendation systems. Although these methods improve the performance of the recommendation system, when the number of users and products increases, the recommendation system may face sparsity and cold start problems, and thus cannot achieve personalized recommendations. Knowledge graphs, which are structured data, have become the choice of many algorithms due to the high quality and wide scale of the data, and therefore many recommendation algorithms combined with knowledge graphs have emerged as a popular new direction in recommendation systems. These algorithms are able to preserve the rich connections between different entities. Moreover, when constructing the features of an entity, the entities that are far away from the central entity can also be utilized. Entities are no longer only directly connected to each other. To address the shortcomings of existing recommendation algorithms, this paper designs the recommendation algorithm GPRE using graph neural networks. GPRE focuses on expressing the user's features. The graph neural network provides GPRE with a strong generalization capability for modeling, which can provide long-range semantics between users and entities, as well as selective entity selection in the auxiliary graph neural network. Explicit semantic links are established between remote and central nodes to reduce the introduction of noise. In this paper, experiments are conducted on real-world datasets and the results are compared with baselines. The experimental results show that GPRE performs well on the experimental dataset.
Keywords
Recommendation; GNN, Feature; Path; Embedding
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
