2 citations · 2 across the 10 of their papers we have counts for
5 papers · 1 filter
An Event is Worth One Token: Event Tokenization for Industrial-scale LLM Recommendation
Fan Xia, Zhaoheng Zheng, Iman Setayesh +12
LLM-based recommendation has scaled along model capacity and sequence length, yet each position encodes only text, semantic IDs, or a few categorical features, discarding rich user…
NEXT: Reasoning-Driven Video Recommendation via a Vision-Language Model
Yuming Liu, Hongye Yang, Harrison Zhao +5
We present NEXT (Next-interest EXploration Transformer), a reasoning-driven video recommendation framework that reasons over the video a user has just watched, infers the viewer's…
Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System
Jiang Zhang, Yubo Wang, Wei Chang +14
Approximate nearest neighbor (ANN) search is widely used in the retrieval stage of large-scale recommendation systems. In this stage, candidate items are indexed using their learne…
Reason to Contrast: A Cascaded Multimodal Retrieval Framework
Xuanming Cui, Hong-You Chen, Hao Yu +10
Traditional multimodal retrieval systems rely primarily on bi-encoder architectures, where performance is closely tied to embedding dimensionality. Recent work, Think-Then-Embed (T…
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
Benyu Zhang, Qiang Zhang, Jianpeng Cheng +10
Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are cr…