Search Results for author: Chuanwei Ruan

Found 14 papers, 5 papers with code

Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System

no code implementations26 Mar 2022 Da Xu, Chuanwei Ruan

Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems.

Causal Inference Decision Making +3

Towards Robust Off-policy Learning for Runtime Uncertainty

no code implementations27 Feb 2022 Da Xu, Yuting Ye, Chuanwei Ruan, Bo Yang

Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment.

Towards the D-Optimal Online Experiment Design for Recommender Selection

1 code implementation23 Oct 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

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 evaluations are prone to the bias of history data.

Multi-Armed Bandits

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

no code implementations23 Oct 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative filtering (NCF), and conjectures the dot product's superiority over the feed-forward neural network as similarity function.

Collaborative Filtering Transductive Learning

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

2 code implementations ICLR 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information.

Density Estimation

Understanding the role of importance weighting for deep learning

no code implementations ICLR 2021 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 models.

Learning Theory

Theoretical Understandings of Product Embedding for E-commerce Machine Learning

no code implementations24 Feb 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The generalization performance in the downstream machine learning task is controlled by the alignment between the embeddings and the product relatedness measure.

BIG-bench Machine Learning Dimensionality Reduction +2

Adversarial Counterfactual Learning and Evaluation for Recommender System

1 code implementation NeurIPS 2020 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

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 exposure mechanism.

Causal Inference counterfactual +1

Inductive Representation Learning on Temporal Graphs

4 code implementations ICLR 2020 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

Graph Attention Graph Embedding +3

Self-attention with Functional Time Representation Learning

2 code implementations NeurIPS 2019 Da Xu, Chuanwei Ruan, Sushant Kumar, Evren Korpeoglu, Kannan Achan

To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces.

Representation Learning Translation

Product Knowledge Graph Embedding for E-commerce

no code implementations28 Nov 2019 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

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.

Knowledge Graph Embedding Marketing +2

Knowledge-aware Complementary Product Representation Learning

no code implementations16 Mar 2019 Da Xu, Chuanwei Ruan, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.

Multi-Task Learning Recommendation Systems +1

Generative Graph Convolutional Network for Growing Graphs

no code implementations6 Mar 2019 Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data.

Graph Generation Graph Reconstruction +1

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