44 citations · 92 across the 9 of their papers we have counts for
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cs.IR2021★ 44 cited
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…
cs.IR2021★ 23 cited
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…
cs.IR2021★ 2 cited
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…