Profile

Niranjan Pedanekar is the Head of Data Science at Sony Research. He was formerly a Chief Scientist and Distinguished Engineer at TCS Research. He holds a Master of Science in Mechanical Engineering from Purdue University. He has more than 25 years of experience in applying AI and Data Science to a variety of domains, such as media and entertainment, education, transportation, manufacturing, defense, and IT. Niranjan is also a theatre playwright-director-actor, and has written, adapted, or translated more than 20 plays. He would like this aspect of his creative work to be reflected in his research.

Message

My current research interests include recommender systems, short-form videos, and user engagement foundation models. The eventual goal of this research is to be able to continually predict and increase the engagement of an individual on online entertainment platforms.

Publications

Transformative Movie Discovery: Large Language Models for Recommendation and Genre Prediction

IEEE Access, 2025
Shubham Raj, Anurag Sharma, Sriparna Saha*, Brijraj Singh*, Niranjan Pedanekar*

In the era of digital streaming platforms, personalized movie recommendations, and genre prediction have become pivotal for enhancing user engagement and satisfaction. With the growing number of OTT (Over-The-Top) platforms like Netflix, Amazon Prime Video, and Disney+, the …

Efficacy of Large Language Models in Predicting Hindi Movies' Attributes: A Comprehensive Survey and Content-Based Analysis

WWW, 2025
Prabir Mondal*, Siddharth Singh*, Kushum*, Sriparna Saha*, Jyoti Prakash Singh*, Brijraj Singh*, Niranjan Pedanekar*

This research explores the efficacy of four state-of-the-art Large Language Models (LLMs): GPT-3.5-turbo-0301, Vicuna, PaLM 2, and Dolly in predicting (i) movie genres using audio transcripts of movie trailers and (ii) meta-information such as director and cast details using…

LLM-BRec: Personalizing Session-based Social Recommendation with LLM-BERT Fusion Framework

SIGIR, 2024
Raksha Jalan, Tushar Prakash, Niranjan Pedanekar*

Recommendation models enhance online user engagement by suggesting personalized content, boosting satisfaction and retention. Session-based Recommender systems (SR) have become a significant approach, focusing on capturing users' short-term preferences for more accurate reco…