Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues
This addresses data sparsity and cold start problems in personalized recommendation systems for e-commerce, social media, and online entertainment, but it is incremental as it builds on existing metric learning approaches.
The paper tackled data sparsity and cold start issues in tag recommendation by proposing a metric learning-based algorithm, which outperformed baseline methods like LRML, CML, and ATF, particularly in accuracy for top recommendations while maintaining robustness.
With the rapid growth of digital information, personalized recommendation systems have become an indispensable part of Internet services, especially in the fields of e-commerce, social media, and online entertainment. However, traditional collaborative filtering and content-based recommendation methods have limitations in dealing with data sparsity and cold start problems, especially in the face of largescale heterogeneous data, which makes it difficult to meet user expectations. This paper proposes a new label recommendation algorithm based on metric learning, which aims to overcome the challenges of traditional recommendation systems by learning effective distance or similarity metrics to capture the subtle differences between user preferences and item features. Experimental results show that the algorithm outperforms baseline methods including local response metric learning (LRML), collaborative metric learning (CML), and adaptive tensor factorization (ATF) based on adversarial learning on multiple evaluation metrics. In particular, it performs particularly well in the accuracy of the first few recommended items, while maintaining high robustness and maintaining high recommendation accuracy.