no code implementations • 9 Apr 2023 • Da Xu, Bo Yang
The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems.
no code implementations • 19 Oct 2022 • Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang
Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.
no code implementations • 22 Sep 2022 • Cheng Jie, Da Xu, Zigeng Wang, Wei Shen
Organic search comprises a large portion of the total traffic for e-commerce companies.
no code implementations • 26 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.
no code implementations • ICLR 2022 • Da Xu, Yuting Ye, Chuanwei Ruan
The interventional nature of recommendation has attracted increasing attention in recent years.
no code implementations • 15 Mar 2022 • Da Xu, Bo Yang
In recent years, the interest in developing adaptive solutions for online testing has grown significantly in the industry.
no code implementations • 27 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.
no code implementations • 3 Feb 2022 • Tianqi Wang, Da Xu, Chengpeng Hao, Pia Addabbo, Danilo Orlando
This letter deals with the problem of clutter edge detection and localization in training data.
1 code implementation • 23 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.
no code implementations • 23 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.
no code implementations • 24 Jun 2021 • Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, Wei Shen
With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies.
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.
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.
no code implementations • 24 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.
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.
no code implementations • 28 Oct 2020 • Da Xu
Tensor regression models, such as CP regression and Tucker regression, have many successful applications in neuroimaging analysis where the covariates are of ultrahigh dimensionality and possess complex spatial structures.
no code implementations • 8 Jun 2020 • Xuezhi Ma, Qiushi Liu, Ning Yu, Da Xu, Sanggon Kim, Zebin Liu, Kaili Jiang, Bryan M. Wong, Ruoxue Yan, Ming Liu
Optical hyperspectral imaging based on absorption and scattering of photons at the visible and adjacent frequencies denotes one of the most informative and inclusive characterization methods in material research.
Super-Resolution Optics Materials Science
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.
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.
no code implementations • 28 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.
no code implementations • 16 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.
no code implementations • 6 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.