3 citations · 5 across the 6 of their papers we have counts for
7 papers
EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment
Haokai Ma, Aoqi Hu, Yueao Xing +5
Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the shared token space. Recent att…
Large Language Model Empowered Recommendation Meets All-domain Continual Pre-Training
Haokai Ma, Yunshan Ma, Ruobing Xie +5
Recent research efforts have investigated how to integrate Large Language Models (LLMs) into recommendation, capitalizing on their semantic comprehension and open-world knowledge f…
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling
Shuo Xu, Haokai Ma, Yunshan Ma +4
Product bundling aims to organize a set of thematically related items into a combined bundle for shipment facilitation and item promotion. To increase the exposure of fresh or over…
Negative Sampling in Recommendation: A Survey and Future Directions
Haokai Ma, Ruobing Xie, Lei Meng +5
Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``in…
Plug-in Diffusion Model for Sequential Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommend…
Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation
Haokai Ma, Zhuang Qi, Xinxin Dong +3
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically in…
Triple Sequence Learning for Cross-domain Recommendation
Haokai Ma, Ruobing Xie, Lei Meng +4
Cross-domain recommendation (CDR) aims to leverage the correlation of users' behaviors in both the source and target domains to improve the user preference modeling in the target d…