SEMCo uses sparse entmax contrastive learning for purely content-based cold-start item recommendation, outperforming standard methods in ranking accuracy.
Cold-start recommendation towards the era of large language models (llms): A comprehensive survey and roadmap
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
NaviAgent decouples task planning from tool execution via a Tool World Navigation Model graph to improve scalability and success rates in LLM agents handling large tool ecosystems.
GrowthGR combines ItemLTV counterfactual prediction with MultiGR generative retrieval and MoPO optimization to deliver 5.3% new item GMV lift and 0.3% overall GMV gain on Taobao production.
Presents an uncertainty-calibrated framework applying risk-averse deboosting to low-active users and UCB exploration to high-active users, with reported gains in retention and diversity on a livestream platform.
Advocates prioritizing explicit contextual feedback in LLM-based recommender systems to improve user preference alignment and explainability.
citing papers explorer
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Sparse Contrastive Learning for Content-Based Cold Item Recommendation
SEMCo uses sparse entmax contrastive learning for purely content-based cold-start item recommendation, outperforming standard methods in ranking accuracy.
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Leveraging Artist Catalogs for Cold-Start Music Recommendation
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
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NaviAgent: Bilevel Planning on Tool Navigation Graph for Large-Scale Orchestration
NaviAgent decouples task planning from tool execution via a Tool World Navigation Model graph to improve scalability and success rates in LLM agents handling large tool ecosystems.
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Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search
GrowthGR combines ItemLTV counterfactual prediction with MultiGR generative retrieval and MoPO optimization to deliver 5.3% new item GMV lift and 0.3% overall GMV gain on Taobao production.
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Uncertainty-Calibrated Recommendations for Low-Active Users
Presents an uncertainty-calibrated framework applying risk-averse deboosting to low-active users and UCB exploration to high-active users, with reported gains in retention and diversity on a livestream platform.
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Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
Advocates prioritizing explicit contextual feedback in LLM-based recommender systems to improve user preference alignment and explainability.