Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation
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arXiv:2607. 10541v1 Announce Type: cross Abstract: Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns.
arXiv:2608.23400v1 Announce Type: cross Abstract: Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via ite...
Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iterative denoising. However, while effective at captu...
arXiv:2607. 23762v1 Announce Type: cross Abstract: Conventional recommenders capture users' preferences by optimizing observed user-item relations, whereas continuous generative recommendation additionally learns the trajectory of synthesizing a target item.
arXiv:2608.22920v1 Announce Type: new Abstract: Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like...
arXiv:2606. 06225v1 Announce Type: cross Abstract: Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependence creates a fundamental cold-start challenge when newly added content has no interaction history.