479 citations · 1.9k across the 94 of their papers we have counts for
8 papers · 1 filter
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Zeyu Cui, Jianxin Ma, Chang Zhou +2
Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise…
Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
Yongji Wu, Defu Lian, Neil Zhenqiang Gong +4
Self-attention has become increasingly popular in a variety of sequence modeling tasks from natural language processing to recommendation, due to its effectiveness. However, self-a…
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention
Yongji Wu, Lu Yin, Defu Lian +4
Sequential recommendation plays an increasingly important role in many e-commerce services such as display advertisement and online shopping. With the rapid development of these se…
Dynamic Memory based Attention Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Ninghao Liu +4
Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and pre…
Sparse-Interest Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao +4
Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical…
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
Chang Zhou, Jianxin Ma, Jianwei Zhang +2
Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial recommen…
Learning to Hash with Graph Neural Networks for Recommender Systems
Qiaoyu Tan, Ninghao Liu, Xing Zhao +3
Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects…
Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang +2
Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommend…