Friday, October 28, 2016

ACM Recsys Challange hosted by Xing.

A month or so ago Xing hosted the ACM RecSys Challenge Workshop in Boston, concluding a great challenge on predicting Job Postings for our jobs market. A selected group of participants including the top 5 winners presented their solution in the workshop. In the following I will present some of my impressions on the challenge and the workshop.

The winners and organisers of the challenge

The Challenge

The details of the challenge can be found in our paper [1], however I will still describe some of the task for the convenience of the reader.  

As some of you know I currently work as a data scientist Xing is a social network in Hamburg for professionals. The network also includes a job market where users can browse one million job offers
from other companies. For the challenge we created a semi- synthetic dataset, handing out anonymised user profiles, job postings as well as interaction data.




The task of the challenge was to predict a users future interactions using the provided dataset.
In order to anonymise the dataset, we created synthetic (random) users and their interactions.

From Xing's perspective the challenge was a huge success with the top solutions providing new insight and solutions into our data. In my mind the "coolest" approach to the challenge was that multiple teams used gradient boosting of trees (xgboost [2]) to predict the users clicks. In both cases
the algorithm was fed user item pairs and their features from the interaction data and then the tree
learned to predict users clicks.  Another team with a high rank used the temporal data to train a recurrent neural network (LSTM).

Happily we are also confirmed to host the next years challenge. For 2017 we plan to release the dataset periodically and evaluate high scoring solutions in our live recommendation system.

All information about the 2016 challenge and the upcoming 2017 challenge can be found at [3] 

References

[1] Abel, Benczur, Kohlsdorf, Larson, Palovics: "RecSys Challenge 2016: Job Recommendations" http://dl.acm.org/citation.cfm?id=2959207

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