12 papers
FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
Zenan Dai, Jinpeng Wang, Junwei Pan +3
Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While fre…
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…
Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation
Junwei Pan, Wei Xue, Chao Zhou +20
Generative recommender systems are rapidly emerging as a new paradigm for recommendation, where collaborative identifiers and/or multi-modal content are mapped into discrete token…
From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models
Mingjia Yin, Junwei Pan, Hao Wang +5
Click-Through Rate (CTR) prediction, a core task in recommendation systems, aims to estimate the probability of users clicking on items. Existing models predominantly follow a disc…
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…
Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
Yuhao Wang, Junwei Pan, Xinhang Li +6
Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large…