Recommendation Systems
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
·2716 words·13 mins·
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AI Applications
Recommendation Systems
🏢 Zhejiang University
Pairwise Softmax Loss (PSL) improves recommendation accuracy by enhancing Softmax Loss (SL) with alternative activation functions, resulting in tighter ranking metric surrogates and better noise resis…
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
·1957 words·10 mins·
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AI Applications
Recommendation Systems
🏢 City University of Hong Kong
LLM-ESR enhances sequential recommendation by integrating semantic information from LLMs, significantly improving performance on long-tail users and items.
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
·2186 words·11 mins·
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AI Applications
Recommendation Systems
🏢 Tsinghua University
SEvo, a novel embedding update mechanism, directly injects graph structural information into recommendation embeddings, boosting performance significantly while avoiding the computational overhead of …
Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
·2088 words·10 mins·
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Recommendation Systems
🏢 Central South University
User Sequence Imagination (USIM) revolutionizes out-of-vocabulary item recommendation by leveraging user sequence imagination and RL fine-tuning, achieving superior performance in real-world e-commerc…
Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation
·2228 words·11 mins·
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AI Applications
Recommendation Systems
🏢 Rochester Institute of Technology
E-NSDE, a novel time-aware sequential recommendation model, integrates neural stochastic differential equations and evidential learning to improve recommendation accuracy by effectively handling varia…
End-to-end Learnable Clustering for Intent Learning in Recommendation
·2462 words·12 mins·
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Machine Learning
Recommendation Systems
🏢 Ant Group
ELCRec: a novel intent learning model for recommendation, unites behavior representation learning with end-to-end learnable clustering, achieving superior performance and scalability.
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
·2288 words·11 mins·
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Machine Learning
Recommendation Systems
🏢 McGill University
GPR4DUR leverages Gaussian Process Regression to create density-based user representations for accurate multi-interest personalized retrieval, overcoming limitations of existing methods.
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
·1801 words·9 mins·
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AI Generated
Natural Language Processing
Recommendation Systems
🏢 University of Science and Technology of China
DDSR: a novel sequential recommendation model uses fuzzy sets and discrete diffusion to capture user behavior randomness, outperforming existing methods.