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Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
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Web of Science
Cited 2 time in Scopus
- Authors
- Issue Date
- 2022-10
- Publisher
- Association for Computing Machinery
- Citation
- Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM 2022, pp.4128-4132
- Abstract
- Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions. However, little attention was paid to GNN's vulnerability to exposure bias: users are exposed to a limited number of items so that a system only learns a biased view of user preference to result in suboptimal recommendation quality. Although inverse propensity weighting is known to recognize and alleviate exposure bias, it usually works on the final objective with the model outputs, whereas GNN can also be biased during neighbor aggregation. In this paper, we propose a simple but effective approach, neighbor aggregation via inverse propensity (Navip) for GNNs. Specifically, given a user-item bipartite graph, we first derive propensity score of each user-item interaction in the graph. Then, inverse of the propensity score with Laplacian normalization is applied to debias neighbor aggregation from exposure bias. We validate the effectiveness of our approach through our extensive experiments on two public and Amazon Alexa datasets where the performance enhances up to 14.2%.
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Related Researcher
- College of Engineering
- Department of Electrical and Computer Engineering
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