44 citations · 69 across the 8 of their papers we have counts for
5 papers · 1 filter
Towards Reliable Item Sampling for Recommendation Evaluation
Dong Li, Ruoming Jin, Zhenming Liu +3
Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a…
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…
Large-scale Real-time Personalized Similar Product Recommendations
Zhi Liu, Yan Huang, Jing Gao +2
Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring…