72 citations · 193 across the 14 of their papers we have counts for
14 papers
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System
Da Xu, Chuanwei Ruan
In the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. The most recent wav…
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation
Da Xu, Yuting Ye, Chuanwei Ruan
The interventional nature of recommendation has attracted increasing attention in recent years. It particularly motivates researchers to formulate learning and evaluating recommend…
Towards Robust Off-policy Learning for Runtime Uncertainty
Da Xu, Yuting Ye, Chuanwei Ruan +1
Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment. However, during the real-time serving, we observe varieties of interv…
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…
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…
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…
Understanding the role of importance weighting for deep learning
Da Xu, Yuting Ye, Chuanwei Ruan
The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning mod…
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…
Adversarial Counterfactual Learning and Evaluation for Recommender System
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposur…
Inductive Representation Learning on Temporal Graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic gr…
Self-attention with Functional Time Representation Learning
Da Xu, Chuanwei Ruan, Sushant Kumar +2
Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and int…
Product Knowledge Graph Embedding for E-commerce
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key…
Knowledge-aware Complementary Product Representation Learning
Da Xu, Chuanwei Ruan, Jason Cho +3
Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, w…
Generative Graph Convolutional Network for Growing Graphs
Da Xu, Chuanwei Ruan, Kamiya Motwani +3
Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existin…