262 citations · 1k across the 32 of their papers we have counts for
32 papers
From a Static Multi-Level Small Semantic Codebook to a Dynamic Single-Level Large Semantic Codebook for Generative Recommendation
Tianlu Xie, Xin Ku, Mingjie Sun +8
Generative recommendation represents each item with a sequence of discrete Semantic IDs (SIDs) and predicts the sequence to retrieve the next item. Typical systems use multi-level…
WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models
Hao Jiang, Peiru Du, Pengfei Yao +10
Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs). Existing approache…
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba
Xinyuan Qi, Kai Hou, Tong Liu +3
Time series forecasting (TSF) is fundamentally required in many real-world applications, such as electricity consumption planning and sales forecasting. In e-commerce, accurate tim…
End-to-End User Behavior Retrieval in Click-Through RatePrediction Model
Qiwei Chen, Changhua Pei, Shanshan Lv +3
Click-Through Rate (CTR) prediction is one of the core tasks in recommender systems (RS). It predicts a personalized click probability for each user-item pair. Recently, researcher…
GRN: Generative Rerank Network for Context-wise Recommendation
Yufei Feng, Binbin Hu, Yu Gong +3
Reranking is attracting incremental attention in the recommender systems, which rearranges the input ranking list into the final rank-ing list to better meet user demands. Most exi…
Revisit Recommender System in the Permutation Prospective
Yufei Feng, Yu Gong, Fei Sun +2
Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite cand…
Explore User Neighborhood for Real-time E-commerce Recommendation
Xu Xie, Fei Sun, Xiaoyong Yang +4
Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been wid…
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8
As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior appro…
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Runsheng Yu, Yu Gong, Xu He +4
A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data c…
Learning User Representations with Hypercuboids for Recommender Systems
Shuai Zhang, Huoyu Liu, Aston Zhang +6
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifical…
Commonsense knowledge adversarial dataset that challenges ELECTRA
Gongqi Lin, Yuan Miao, Xiaoyong Yang +4
Commonsense knowledge is critical in human reading comprehension. While machine comprehension has made significant progress in recent years, the ability in handling commonsense kno…
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Binbin Hu +5
Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences…
Understanding Echo Chambers in E-commerce Recommender Systems
Yingqiang Ge, Shuya Zhao, Honglu Zhou +4
Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper item…
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
Wenhui Yu, Xiao Lin, Junfeng Ge +2
Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designi…
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Yufei Feng, Binbin Hu, Fuyu Lv +3
Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts m…
EdgeRec: Recommender System on Edge in Mobile Taobao
Yu Gong, Ziwen Jiang, Yufei Feng +4
Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recomme…
Privileged Features Distillation at Taobao Recommendations
Chen Xu, Quan Li, Junfeng Ge +7
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the…
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
Yu Zhu, Yu Gong, Qingwen Liu +6
Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively interven…
Exact-K Recommendation via Maximal Clique Optimization
Yu Gong, Yu Zhu, Lu Duan +5
This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (const…
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Qiwei Chen, Huan Zhao, Wei Li +2
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dim…
Compositional Network Embedding
Tianshu Lyu, Fei Sun, Peng Jiang +2
Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existin…
Personalized Re-ranking for Recommendation
Changhua Pei, Yi Zhang, Yongfeng Zhang +6
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optim…
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Fei Sun, Jun Liu, Jian Wu +4
Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural ne…
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
Changhua Pei, Xinru Yang, Qing Cui +5
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-…