Author

Shreyaa Rajpal

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#artificial intelligence Preprint Oct 2026

Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation

Recommender systems learn from observed user-item interactions, but explicit negative feedback is often unavailable. Since deep learning models require negative signals for training, negative sampling methods typically treat selected unobserved interactions as negatives. However, a missing interaction does not explain...

Shreyaa Rajpal, Sonia Sharma, Swapnil Parekh et al. · 0 citations

Preprint Aug 2026

From Gradient-Boosted Trees to Deep Recommenders: Practical Lessons from Migrating a Production Customer Support Recommender

Product catalogs in fast-moving service businesses are shifting from static, independently priced SKUs toward dynamically bundled, discount-coupled offerings--a shift that strains the tree-based classifiers traditionally preferred for sparse and highly imbalanced data. These classifiers assume a fixed, slowly changing...

Sonia Sharma, Jeyendran Balakrishnan, Shreyaa Rajpal et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale

Simulation made broad exploration of models, reasoning settings, and prompts feasible without customer exposure, enabling production improvements that would have been impractical to pursue through live experimentation alone.

Edesio Alcoba, Kevin Rossell, Aman Gupta et al. · 0 citations