Collaborative Filtering
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Benchmarks
Most implemented
Neural Collaborative Filtering
Neural Graph Collaborative Filtering
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Variational Autoencoders for Collaborative Filtering
Graph Convolutional Matrix Completion
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Papers
Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach
Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start regime, and intrinsic musical content, available for any recording. We argue that a third, largely untapped signa…
Collaborative FilteringAdaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation
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 capturing user-level sequ…
Collaborative FilteringRecommendation SystemsUniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
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. Production s…
Collaborative FilteringPOI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment
Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, whe…
Multimodal RecommendationCollaborative FilteringGraph Neural NetworkContrastive LearningKairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world news has a short Time-to-Live (TTL < 48 h) and shallow article pools.…
Representation LearningCollaborative FilteringRecommendation SystemsTailorMind: Towards Preference-Aligned Multimodal Content Generation
Personalized content systems depend on available UGC and struggle when suitable content is absent, delayed, or costly to create. Although multimodal generators can synthesize content on demand, how to translate behaviora…
Collaborative Filteringmultimodal generation