BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
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A human-centered design workshop with journalism practitioners yields an evaluation cookbook and design requirements for contextualized, value-aligned generative AI benchmarks.
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BIPCL: Bilateral Intent-Enhanced Sequential Recommendation via Embedding Perturbation Contrastive Learning
BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
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Towards Real-World Validity in Generative AI Benchmarks: Understanding and Designing Domain-Centered Evaluations for Journalism Practitioners
A human-centered design workshop with journalism practitioners yields an evaluation cookbook and design requirements for contextualized, value-aligned generative AI benchmarks.