73 citations · 130 across the 13 of their papers we have counts for
13 papers
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Renzhi Wu, Zikun Cui, Junjie Yang +10
Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet exi…
CMSL: Constructive Multi-Sequence Learning for Recommendation Systems
Zikun Cui, Renzhi Wu, Junjie Yang +10
Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuanc…
RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
Renzhi Wu, Junjie Yang, Li Chen +3
Cross-domain recommendation systems face the challenge of integrating fine-grained user and item relationships across various product domains. To address this, we introduce RankGra…
SketchQL Demonstration: Zero-shot Video Moment Querying with Sketches
Renzhi Wu, Pramod Chunduri, Dristi J Shah +5
In this paper, we will present SketchQL, a video database management system (VDBMS) for retrieving video moments with a sketch-based query interface. This novel interface allows us…
Decentralized Personalized Online Federated Learning
Renzhi Wu, Saayan Mitra, Xiang Chen +1
Vanilla federated learning does not support learning in an online environment, learning a personalized model on each client, and learning in a decentralized setting. There are exis…
Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data
Renzhi Wu, Jingfan Meng, Jie Jeff Xu +2
In this vision paper, we propose a shift in perspective for improving the effectiveness of similarity search. Rather than focusing solely on enhancing the data quality, particularl…
Ground Truth Inference for Weakly Supervised Entity Matching
Renzhi Wu, Alexander Bendeck, Xu Chu +1
Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine…
Learning Hyper Label Model for Programmatic Weak Supervision
Renzhi Wu, Shen-En Chen, Jieyu Zhang +1
To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model t…
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
Renzhi Wu, Bolin Ding, Xu Chu +4
Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus…
Demonstration of Panda: A Weakly Supervised Entity Matching System
Renzhi Wu, Prem Sakala, Peng Li +2
Entity matching (EM) refers to the problem of identifying tuple pairs in one or more relations that refer to the same real world entities. Supervised machine learning (ML) approach…
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Bojan Karlaš, Peng Li, Renzhi Wu +4
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubi…
ZeroER: Entity Resolution using Zero Labeled Examples
Renzhi Wu, Sanya Chaba, Saurabh Sawlani +2
Entity resolution (ER) refers to the problem of matching records in one or more relations that refer to the same real-world entity. While supervised machine learning (ML) approache…
GOGGLES: Automatic Image Labeling with Affinity Coding
Nilaksh Das, Sanya Chaba, Renzhi Wu +3
Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has…