In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning.

Machine learning (ML) is pervasively deployed.

Sequential recommender systems are designed to capture users' evolving interests

Suppose a revenue-maximizing recommendation algorithm concludes from past data that more revenue is generated by showing the ad to Group A compared to Group B. In that case, the ad-serving algorithm will eventually end up showing that ad exclusively to Group A

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings.

KuaiRand Dataset and compare them against KuaiSim to further assess their performance and behavioral differences.

This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between entities, hindering deeper insights into graph-structured data.