Combining sequential and graph representations through multi-view contrastive learning and attention fusion significantly improves sequential recommendation accuracy, showing that different data perspectives can be effectively integrated for better predictions.
This paper proposes MVCrec, a recommendation system that learns from user interaction histories by combining two complementary views: sequential ID-based patterns and graph-based relational structures. Using contrastive learning across both views and a multi-view attention mechanism to fuse them, the approach achieves significant improvements on benchmark datasets without requiring external data.