Evaluate DSRec in cross-domain recommendation
Evaluate DSRec in cross-domain sequential recommendation settings to assess whether its dual-interest, multi-granular state-space architecture has broader applications beyond the tested datasets and domains.
References
While DSRec achieves advanced performance, several limitations remain open for future research. First, the item polysemous modeling is novel but basic, we plan to extend by context aware dual-tokenization to incorporate item-side semantic roles, such as Variational AutoEncoder (VAE). Second, sparse context information in transactions need the integration of external knowledge (such as graph structure) into the interest embedding, which may further enhance generalization. Finally, exploring a learnable fusion gate or contrastive alignment between the two semantic branches could yield more adaptive interactions beyond residual fusion. We also intend to test DSRec in cross-domain settings to assess its broader applications.