5 citations · 17 across the 26 of their papers we have counts for
9 papers · 1 filter
EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens
Chaoqun Yang, Xinyu Lin, Wenjie Wang +4
Large Language Model-based generative recommendation (LLMRec) has achieved notable success, but it suffers from high inference latency due to massive computational overhead and mem…
Rec: Towards Large Recommender Models with Reasoning
Runyang You, Yongqi Li, Xinyu Lin +4
Large recommender models have extended LLMs as powerful recommenders via encoding or item generation, and recent breakthroughs in LLM reasoning synchronously motivate the explorati…
Exploring Training and Inference Scaling Laws in Generative Retrieval
Hongru Cai, Yongqi Li, Ruifeng Yuan +4
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel par…
Efficient Inference for Large Language Model-based Generative Recommendation
Xinyu Lin, Chaoqun Yang, Wenjie Wang +5
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caus…
Learnable Item Tokenization for Generative Recommendation
Wenjie Wang, Honghui Bao, Xinyu Lin +5
Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data in…
A Survey of Generative Search and Recommendation in the Era of Large Language Models
Yongqi Li, Xinyu Lin, Wenjie Wang +6
With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, bot…
Data-efficient Fine-tuning for LLM-based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li +4
Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost o…
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
Xinyu Lin, Wenjie Wang, Jujia Zhao +3
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address…
Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li +3
Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space…