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20212026
most citedRethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives

3 citations · 14 across the 22 of their papers we have counts for

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5 papers · 1 filter

cs.IR2021★ 3 cited

Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives

Da Xu, Chuanwei Ruan, Evren Korpeoglu +2

The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative…

cs.IR2021★ 2 cited

Towards the D-Optimal Online Experiment Design for Recommender Selection

Da Xu, Chuanwei Ruan, Evren Korpeoglu +2

Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluat…

cs.IR2021★ 1 cited

Variational Inference for Category Recommendation in E-Commerce platforms

Ramasubramanian Balasubramanian, Venugopal Mani, Abhinav Mathur +2

Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website. It is therefore important to surface precis…

cs.LG2021★ 1 cited

A Temporal Kernel Approach for Deep Learning with Continuous-time Information

Da Xu, Chuanwei Ruan, Evren Korpeoglu +2

Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information. Discretizing the temporal data, as we show, caus…

cs.LG2021

Theoretical Understandings of Product Embedding for E-commerce Machine Learning

Da Xu, Chuanwei Ruan, Evren Korpeoglu +2

Product embeddings have been heavily investigated in the past few years, serving as the cornerstone for a broad range of machine learning applications in e-commerce. Despite the em…