22 citations · 85 across the 16 of their papers we have counts for
6 papers · 1 filter
Zero-Indexing Internet Search Augmented Generation for Large Language Models
Guangxin He, Zonghong Dai, Jiangcheng Zhu +6
Retrieval augmented generation has emerged as an effective method to enhance large language model performance. This approach typically relies on an internal retrieval module that u…
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
Linhao Luo, Yumeng Li, Buyu Gao +7
Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendat…
RNE: A Scalable Network Embedding for Billion-scale Recommendation
Jianbin Lin, Daixin Wang, Lu Guan +5
Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dy…
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
Wen Chen, Pipei Huang, Jiaming Xu +7
Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashi…
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Yong Liu, Yinan Zhang, Qiong Wu +5
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years. Most previous…
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
Jizhe Wang, Pipei Huang, Huan Zhao +3
Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. The billio…