1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Quantizing Intent: Cross-Domain Semantic IDs from Organic Activity for Industrial Ranking
Julie Choi, Haoran Ye, Zhiwei Ding +3
Ads click-through rate (CTR) prediction is constrained by sparse user supervision: most users engage with ads infrequently while generating dense behavioral evidence in organic sur…
Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures
Rahul Raja, Arpita Vats
Question Answering (QA) systems have traditionally relied on structured text data, but the rapid growth of multimedia content (images, audio, video, and structured metadata) has in…
A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
Rahul Raja, Anshaj Vats, Arpita Vats +1
Recommender systems have traditionally followed modular architectures comprising candidate generation, multi-stage ranking, and re-ranking, each trained separately with supervised…
Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
Arpita Vats, Vinija Jain, Rahul Raja +1
The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional…