arXiv AI
Jul 7
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.
Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation
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arXiv AI
6d ago
Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation
arXiv:2608.21243v1 Announce Type: cross Abstract: Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combi...
arXiv AI
Jul 21
WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture
arXiv:2607. 17017v1 Announce Type: cross Abstract: As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate paths: non-sequence features, including user, item, context, and cross features; and sequence features from user behavior histories.
arXiv AI
Aug 18
UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
arXiv:2608. 16797v1 Announce Type: cross Abstract: Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories.
Hugging Face Trending Papers
Jun 9
Generative Archetype-Grounded Item Representations for Sequential Recommendation
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.
arXiv Machine Learning
Jun 10
Generative Archetype-Grounded Item Representations for Sequential Recommendation
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv Machine Learning
Jul 14
RecRec: Recursive Refinement for Sequential Recommendation
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 Machine Learning
Jul 17
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
arXiv:2607. 14192v1 Announce Type: new Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention.
arXiv AI
Aug 10
Towards a Theoretical Understanding of Two Tower Recommendation Models
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
arXiv AI
Jul 15
Action-Aware Generative Sequence Modeling for Short Video Recommendation
arXiv:2604. 25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms.
arXiv Machine Learning
Aug 21
RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations
arXiv:2608. 19735v1 Announce Type: new Abstract: We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation.
arXiv AI
Aug 17
EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment
arXiv:2608. 14011v1 Announce Type: cross Abstract: Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the shared token space.