Reference Implementation for WSDM 2018 Paper "Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering"

TensorFlow implementation of Z. Hu et al. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction", WSDM 2018

Code for "An Online Algorithm to Reduce the Spread of Misinformation in Social Networks", WSDM 2018

The 11th ACM International Conference on Web Search and Data Mining (WSDM 2018) is challenging you to build an algorithm that predicts whether a subscription user will churn using a donated dataset from KKBOX.

Exploring Expert Cognition for Attributed Network Embedding, WSDM, 2018

:musical_score: Predict the chances of a user listening to a song repetitively after the first observable listening event within a time window was triggered.

WSDM-2018

This block is some papers how to utilize complex temporal information in Sequential Recommendation or Session-base Recommendation At present, we have collected about twenty to thirty papers, mainly about KDD、CIKM、AAAI、SIGIR、WWW、WSDM、IJCAI, years:2018--2020

WSDM - KKBox's Churn Prediction Challenge / Can you predict when subscribers will churn?

Publication of the code we used in the WSDM'18 Cup

Website of the Neural Networks for Information Retrieval tutorial at SIGIR 2017, WSDM 2018 and ECIR 2018.

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Website for KBCOM 2018 Workshop at WSDM'18

This is a solution for 2018 WSDM competetion: KKBox's Music Recommendation Challenge

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WSDM - KKBox's Churn Prediction Challenge / Can you predict when subscribers will churn?

Repository for WSDM 2018 Music Recommendation Challenge

Pytorch code of Kaggle WSDM Fake news classification 2018.

This is Music recommendation challenge of KKBOX WSDM 2018