Rethinking negative sampling in content-based news recommendation
This addresses the challenge of brief article lifespans in news recommender systems, though it appears incremental as it builds on existing content-based neural techniques.
The paper tackles the problem of rapid relevance decay in news recommendation by proposing a negative sampling technique that improves model accuracy while reducing complexity and accelerating training, achieving competitive accuracy with State-of-the-Art models on the MIND dataset.
News recommender systems are hindered by the brief lifespan of articles, as they undergo rapid relevance decay. Recent studies have demonstrated the potential of content-based neural techniques in tackling this problem. However, these models often involve complex neural architectures and often lack consideration for negative examples. In this study, we posit that the careful sampling of negative examples has a big impact on the model's outcome. We devise a negative sampling technique that not only improves the accuracy of the model but also facilitates the decentralization of the recommendation system. The experimental results obtained using the MIND dataset demonstrate that the accuracy of the method under consideration can compete with that of State-of-the-Art models. The utilization of the sampling technique is essential in reducing model complexity and accelerating the training process, while maintaining a high level of accuracy. Finally, we discuss how decentralized models can help improve privacy and scalability.