From the 1 of 10 linked papers with an AI index.
10 papers
ATLAS: Learning to Recommend Across Unseen Domains
Pervez Shaik, Prosenjit Biswas, Abhinav Thorat +2
Recommender systems remain domain-bound: a model trained on one interaction environment typically requires retraining or target-domain adaptation before it can operate on a new cat…
GENESIS: Towards Explainable Causal Discovery
Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat +2
Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve structural ambiguities in low-samp…
RecRec: Recursive Refinement for Sequential Recommendation
Pervez Shaik, Prosenjit Biswas, Abhinav Thorat +2
The paper introduces RecRec, a lightweight sequential recommendation model that iteratively refines a compact latent user state using a shared recursive module with an evidence-anc…
Obliviate: Efficient Unlearning in Recommender Systems
Tushar Prakash, Brijraj Singh, Niranjan Pedanekar +1
Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interacti…
PlotTwist: A Creative Plot Generation Framework with Small Language Models
Abhinav Thorat, Ravi Kolla, Jyotin Goel +2
Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative that sustains global coherence, character de…
From What to Why: Thought-Space Recommendation with Small Language Models
Prosenjit Biswas, Pervez Shaik, Abhinav Thorat +2
Large Language Models (LLMs) have advanced recommendation capabilities through enhanced reasoning, but pose significant challenges for real-world deployment due to high inference c…