User-item shilling attacks affect highly-active and slightly-active users in opposite directions depending on dataset sparsity, with highly-active users more vulnerable on sparse Yelp and slightly-active users more vulnerable on denser MovieLens.
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4 Pith papers cite this work, alongside 345 external citations. Polarity classification is still indexing.
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SASRecLLM, a hybrid of SASRec and a LoRA-fine-tuned LLM, reports modest gains over TALLRec on MovieLens but shows inconsistent and contradictory results on Amazon and in warm-start settings.
Tutorial on a GP-based framework for preference and choice learning that unifies random utility models, limits of discernment, and multi-utility scenarios via customized likelihoods for object and label preferences.
LensKit-Auto is updated for compatibility with the latest LensKit, adding Tree Parzen Estimator optimization, algorithm reuse, visualization, documentation, and meta-learning dataset preparation.
citing papers explorer
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Assessing the Impact of a User-Item Collaborative Attack on Class of Users
User-item shilling attacks affect highly-active and slightly-active users in opposite directions depending on dataset sparsity, with highly-active users more vulnerable on sparse Yelp and slightly-active users more vulnerable on denser MovieLens.
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When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs
SASRecLLM, a hybrid of SASRec and a LoRA-fine-tuned LLM, reports modest gains over TALLRec on MovieLens but shows inconsistent and contradictory results on Amazon and in warm-start settings.
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A tutorial on learning from preferences and choices with Gaussian Processes
Tutorial on a GP-based framework for preference and choice learning that unifies random utility models, limits of discernment, and multi-utility scenarios via customized likelihoods for object and label preferences.
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LensKit-Auto
LensKit-Auto is updated for compatibility with the latest LensKit, adding Tree Parzen Estimator optimization, algorithm reuse, visualization, documentation, and meta-learning dataset preparation.