Version 1
: Received: 7 November 2022 / Approved: 7 November 2022 / Online: 7 November 2022 (08:38:06 CET)
How to cite:
brook, M.; Rohlf, D.; Bishop, A.; Jones, M. A Graph Neural Network-based Video Recommendation Model Combining Users’ Long-term and Short-term Preference. Preprints2022, 2022110111. https://doi.org/10.20944/preprints202211.0111.v1
brook, M.; Rohlf, D.; Bishop, A.; Jones, M. A Graph Neural Network-based Video Recommendation Model Combining Users’ Long-term and Short-term Preference. Preprints 2022, 2022110111. https://doi.org/10.20944/preprints202211.0111.v1
brook, M.; Rohlf, D.; Bishop, A.; Jones, M. A Graph Neural Network-based Video Recommendation Model Combining Users’ Long-term and Short-term Preference. Preprints2022, 2022110111. https://doi.org/10.20944/preprints202211.0111.v1
APA Style
brook, M., Rohlf, D., Bishop, A., & Jones, M. (2022). A Graph Neural Network-based Video Recommendation Model Combining Users’ Long-term and Short-term Preference. Preprints. https://doi.org/10.20944/preprints202211.0111.v1
Chicago/Turabian Style
brook, M., Andrea Bishop and Matt Jones. 2022 "A Graph Neural Network-based Video Recommendation Model Combining Users’ Long-term and Short-term Preference" Preprints. https://doi.org/10.20944/preprints202211.0111.v1
Abstract
With the rapid development of technology and the advancement of Internet technology, various social networking platforms are gradually coming into people's view and occupying a higher and higher position. In the recommendation scenario, the user-item interaction naturally forms a bipartite heterogeneous graph structure and with the development of graph embedding and graph neural network technologies based on deep learning to process graph domain information, the combination of graph information and recommendation systems shows strong research potential and application prospects. The methodological improvement of the recommendation algorithm based on collaborative filtering takes advantage of the nature that user-items can form a bipartite graph in the recommendation scenario. The existing methods still have some shortcomings. The methods that only use weights or convolutional recurrent neural networks to implicitly model different historical behaviors lack explicit modeling of video switching relationships in serialized behaviors. The user's interest is changing all the time, so it is not possible to recommend based on the user's history, and it is necessary to consider both the long-term and short-term interest of the user according to the video content in order to achieve accurate recommendation of short videos. In this paper, we design a recommendation model based on graph neural network, which models users' long-term and short-term interests by two vector propagation methods, respectively.
Keywords
Recommendation, GNN, Preference
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.
