Version 1
: Received: 17 October 2024 / Approved: 17 October 2024 / Online: 17 October 2024 (14:41:54 CEST)
How to cite:
Yan, H.; Tao, Z. J.; Zhong, L. Z.; Yang, W. D.; Rui, D. C. Recommendation Algorithm for Graph Convolutional Collaborative Filtering Based on Multivariate Sampling. Preprints2024, 2024101410. https://doi.org/10.20944/preprints202410.1410.v1
Yan, H.; Tao, Z. J.; Zhong, L. Z.; Yang, W. D.; Rui, D. C. Recommendation Algorithm for Graph Convolutional Collaborative Filtering Based on Multivariate Sampling. Preprints 2024, 2024101410. https://doi.org/10.20944/preprints202410.1410.v1
Yan, H.; Tao, Z. J.; Zhong, L. Z.; Yang, W. D.; Rui, D. C. Recommendation Algorithm for Graph Convolutional Collaborative Filtering Based on Multivariate Sampling. Preprints2024, 2024101410. https://doi.org/10.20944/preprints202410.1410.v1
APA Style
Yan, H., Tao, Z. J., Zhong, L. Z., Yang, W. D., & Rui, D. C. (2024). Recommendation Algorithm for Graph Convolutional Collaborative Filtering Based on Multivariate Sampling. Preprints. https://doi.org/10.20944/preprints202410.1410.v1
Chicago/Turabian Style
Yan, H., Wang Dong Yang and Ding Cheng Rui. 2024 "Recommendation Algorithm for Graph Convolutional Collaborative Filtering Based on Multivariate Sampling" Preprints. https://doi.org/10.20944/preprints202410.1410.v1
Abstract
In the field of recommender systems, difficult negative samples that are easily misclassified by classifiers have higher learning value. In order to solve the problem that the existing graph convolutional network lacks the exploration of negative sampling strategy, a multivariate sampling based on graph convolution collaborative filtering recommendation model was proposed. Firstly, part of the information in the positive sample was injected into the negative sample, and the high quality difficult negative sample was selected by the inner product method. Then, contrastive learning is used to maximize the similarity between the difficult negative samples and the positive samples, so that the difficult negative samples are as close to the positive samples as possible in the feature space. Ultimately, a multi-task approach was employed to concurrently enhance both the recommendation supervision task and the contrastive learning task. To validate the effectiveness of our approach, we conducted comparative evaluations utilizing three publicly accessible datasets. The experimental outcomes indicate that our proposed model surpasses the baseline model in terms of performance.
Keywords
graph convolutional networks; multivariate sampling; contrast learning; collaborative filtering; hard to negative sample
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
