activity
20232026
most citedLLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models

2 citations · 5 across the 14 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.AI2025

To See or To Read: User Behavior Reasoning in Multimodal LLMs

Tianning Dong, Luyi Ma, Varun Vasudevan +3

Multimodal Large Language Models (MLLMs) are reshaping how modern agentic systems reason over sequential user-behavior data. However, whether textual or image representations of us…

cs.IR2025

MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems

Shreeranjani Srirangamsridharan, Ali Abavisani, Reza Yousefi Maragheh +4

Meta titles and descriptions strongly shape engagement in search and recommendation platforms, yet optimizing them remains challenging. Search engine ranking models are black box e…

cs.CL2025

GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization

Luyi Ma, Wanjia Zhang, Kai Zhao +15

Generative models have recently demonstrated strong potential in multi-behavior recommendation systems, leveraging the expressive power of transformers and tokenization to generate…

cs.IR2025

CARTS: Collaborative Agents for Recommendation Textual Summarization

Jiao Chen, Kehui Yao, Reza Yousefi Maragheh +6

Current recommendation systems often require some form of textual data summarization, such as generating concise and coherent titles for product carousels or other grouped item dis…

cs.AI2025

Rethinking DPO: The Role of Rejected Responses in Preference Misalignment

Jay Hyeon Cho, JunHyeok Oh, Myunsoo Kim +1

Direct Preference Optimization (DPO) is a simple and efficient framework that has attracted substantial attention. However, it often struggles to meet its primary objectives -- inc…

cs.IR2025★ 1 cited

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation

Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta +7

Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RA…

cs.CL2025

LLM-driven Constrained Copy Generation through Iterative Refinement

Varun Vasudevan, Faezeh Akhavizadegan, Abhinav Prakash +5

Crafting a marketing message (copy), or copywriting is a challenging generation task, as the copy must adhere to various constraints. Copy creation is inherently iterative for huma…