Author

Swapnil Parekh

h-index 6 View on Semantic Scholar

We have 3 of 15 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#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 May 2026

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

ProFIL (**Pro**be-**Filtered Reinforcement Learning) is introduced to reduce theater, increase chain-of-thought faithfulness, and shrink chain length in a single, drop-in extension to Group Relative Policy Optimization (GRPO).

Swapnil Parekh, Naman Goyal · arXiv.org · 2 citations