44 citations · 168 across the 17 of their papers we have counts for
17 papers
Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism
Zhen Peng, Minjia Zhang, Kai Li +2
Nearest Neighbor Search (NNS) has recently drawn a rapid increase of interest due to its core role in managing high-dimensional vector data in data science and AI applications. The…
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Xiang Li, Dong Li, Ruoming Jin +2
Web-based interactions can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successful…
On Sampling Top-K Recommendation Evaluation
Dong Li, Ruoming Jin, Jing Gao +1
Recently, Rendle has warned that the use of sampling-based top- metrics might not suffice. This throws a number of recent studies on deep learning-based recommendation algorithm…
Towards a Better Understanding of Linear Models for Recommendation
Ruoming Jin, Dong Li, Jing Gao +3
Recently, linear regression models, such as EASE and SLIM, have shown to often produce rather competitive results against more sophisticated deep learning models. On the other side…
On Estimating Recommendation Evaluation Metrics under Sampling
Ruoming Jin, Dong Li, Benjamin Mudrak +2
Since the recent study (Krichene and Rendle 2020) done by Krichene and Rendle on the sampling-based top-k evaluation metric for recommendation, there has been a lot of debates on t…
Pruned Landmark Labeling Meets Vertex Centric Computation: A Surprisingly Happy Marriage!
Ruoming Jin, Zhen Peng, Wendell Wu +3
In this paper, we study how the Pruned Landmark Labeling (PPL) algorithm can be parallelized in a scalable fashion, producing the same results as the sequential algorithm. More spe…