Skip to main content
SpringerLink
Log in

Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Yuxiao Dong

    1. Microsoft Research, Redmond, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Dunja Mladenić

    1. Jožef Stefan Institute, Ljubljana, Slovenia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Craig Saunders

    1. Amazon Alexa Knowledge, Cambridge, UK

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12460)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ECML PKDD 2020.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (34 papers)

  1. Front Matter

    Pages i-xlii
    Download chapter PDF

  2. Applied Data Science: Recommendation

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Social Influence Attentive Neural Network for Friend-Enhanced Recommendation

      • Yuanfu Lu, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang et al.
      Pages 3-18

    3. Feedback-Guided Attributed Graph Embedding for Relevant Video Recommendation

      • Taofeng Xue, Xinzhou Dong, Wei Zhuo, Beihong Jin, He Chen, Wenhai Pan et al.
      Pages 19-35

    4. Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach

      • Bowen Hao, Jing Zhang, Cuiping Li, Hong Chen, Hongzhi Yin
      Pages 36-51

    5. Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application

      • Mingxuan Yue, Tianshu Sun, Fan Wu, Lixia Wu, Yinghui Xu, Cyrus Shahabi
      Pages 52-68

    6. Strategic and Crowd-Aware Itinerary Recommendation

      • Junhua Liu, Kristin L. Wood, Kwan Hui Lim
      Pages 69-85

  3. Applied Data Science: Anomaly Detection

    1. Front Matter

      Pages 87-87
      Download chapter PDF

    2. A Context-Aware Approach to Detect Abnormal Human Behaviors

      • Roghayeh Mojarad, Ferhat Attal, Abdelghani Chibani, Yacine Amirat
      Pages 89-104

    3. RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHR

      • Wenjie Wang, Pengfei Tang, Li Xiong, Xiaoqian Jiang
      Pages 105-121

    4. Self-supervised Log Parsing

      • Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, Odej Kao
      Pages 122-138

    5. Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis

      • Dilusha Weeraddana, Sudaraka MallawaArachchi, Tharindu Warnakula, Zhidong Li, Yang Wang
      Pages 139-156

    6. Forecasting Error Pattern-Based Anomaly Detection in Multivariate Time Series

      • Seoyoung Park, Siho Han, Simon S. Woo
      Pages 157-172

  4. Applied Data Science: Web Mining

    1. Front Matter

      Pages 173-173
      Download chapter PDF

    2. Neural User Embedding from Browsing Events

      • Mingxiao An, Sundong Kim
      Pages 175-191

    3. 6VecLM: Language Modeling in Vector Space for IPv6 Target Generation

      • Tianyu Cui, Gang Xiong, Gaopeng Gou, Junzheng Shi, Wei Xia
      Pages 192-207

    4. Calibrating User Response Predictions in Online Advertising

      • Chao Deng, Hao Wang, Qing Tan, Jian Xu, Kun Gai
      Pages 208-223

    5. An Advert Creation System for 3D Product Placements

      • Ivan Bacher, Hossein Javidnia, Soumyabrata Dev, Rahul Agrahari, Murhaf Hossari, Matthew Nicholson et al.
      Pages 224-239

    6. Estimating Precisions for Multiple Binary Classifiers Under Limited Samples

      • Rahul Tripathi, Srinivasan Jagannathan, Balaji Dhamodharaswamy
      Pages 240-256

  5. Applied Data Science: Transportation

    1. Front Matter

      Pages 257-257
      Download chapter PDF
Back to top

Other volumes

  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

  3. Machine Learning and Knowledge Discovery in Databases

  4. Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

  5. Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

  6. ECML PKDD 2020 Workshops

Keywords

About this book

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.

The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings.

The volumes are organized in topical sections as follows:

Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion.

Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning.

Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics.

Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data.

Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

 

 

Editors and Affiliations

  • Microsoft Research, Redmond, USA

    Yuxiao Dong

  • Jožef Stefan Institute, Ljubljana, Slovenia

    Dunja Mladenić

  • Amazon Alexa Knowledge, Cambridge, UK

    Craig Saunders

Bibliographic Information

Publish with us

Policies and ethics

Societies and partnerships

Back to top

Search

Navigation