6 papers
Diffusion Language Model for Recommendation
Chengyi Liu, Yongqi Zhou, Junwei Pan +8
Large language model (LLM)-empowered recommender systems have emerged as a promising paradigm for generative recommendation, leveraging their strong semantic reasoning and generati…
Asymmetric Generative Recommendation via Kronecker Residual Bridge and Multi-Faceted Hierarchical Quantization
Bin Huang, Xin Wang, Junwei Pan +6
Generative Recommendation (GenRec) models reformulate recommendation as a sequence generation task, representing items as discrete Semantic IDs used symmetrically as both inputs an…
TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
Yifeng Zhou, Yuehong Hu, Zhixiang Feng +9
Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among multi-field categorical features…
Practice on Long Behavior Sequence Modeling in Tencent Advertising
Xian Hu, Ming Yue, Zhixiang Feng +24
Long-sequence modeling has become an indispensable frontier in recommendation systems for capturing users' long-term preferences. However, user behaviors within advertising domains…
Large Foundation Model for Ads Recommendation
Shangyu Zhang, Shijie Quan, Zhongren Wang +30
Online advertising relies on accurate recommendation models, with recent advances using pre-trained large-scale foundation models (LFMs) to capture users' general interests across…
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
Yuhao Wang, Junwei Pan, Pengyue Jia +6
Sequential Recommendation (SR) aims to leverage the sequential patterns in users' historical interactions to accurately track their preferences. However, the primary reliance of ex…