5 citations · 9 across the 5 of their papers we have counts for
5 papers
Masked Diffusion Generative Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +4
Generative recommendation (GR) typically first quantizes continuous item embeddings into multi-level semantic IDs (SIDs), and then generates the next item via autoregressive decodi…
Synergistic Integration and Discrepancy Resolution of Contextualized Knowledge for Personalized Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +7
The integration of large language models (LLMs) into recommendation systems has revealed promising potential through their capacity to extract world knowledge for enhanced reasonin…
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation
Hao Deng, Haibo Xing, Kanefumi Matsuyama +8
Transformer-based sequential recommendation (TSR) models have shown superior performance in recommendation systems, where the quality of item representations plays a crucial role.…
CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval
Hao Deng, Haibo Xing, Kanefumi Matsuyama +6
Multi-objective embedding-based retrieval (EBR) has become increasingly critical due to the growing complexity of user behaviors and commercial objectives. While traditional approa…
ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems
Haibo Xing, Kanefumi Matsuyama, Hao Deng +3
Industrial recommendation systems typically involve a two-stage process: retrieval and ranking, which aims to match users with millions of items. In the retrieval stage, classic em…