Workshop on the RecSys Challenge 2015

YOOCHOOSEIn this year’s edition of the RecSys Challenge, YOOCHOOSE provided a collection of sequences of click events; click sessions. The goal of the challenge was to predict whether a user is going to buy something or not, and if he is buying, what would be the items he is going to buy.
During this workshop, accepted contributions are presented.

Schedule and Accepted Contributions
Sept 16
09:00-09:20 Overview
by Michael Friedmann and David Ben-Shimon
09:20-09:40 RecSys Challenge 2015: ensemble learning with categorical features
by Peter Romov and Evgeny Sokolov
09:40-10:00 E-Commerce Item Recommendation Based on Field-aware Factorization Machine
by Peng Yan, Xiaocong Zhou and Yitao Duan
10:00-10:20 Two-Stage Approach to Item Recommendation from User Sessions
by Maksims Volkovs
10:20-10:40 Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization
by Robert Palovics, Levente Kocsis, Peter Szalai, Julia Pap, Adrienn Szabo, Zsofia Klara Nyikes and Andras A. Benczur
10:40-11:00 Break
11:00-11:20 Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data
by Oyvind Myklatun, Thorstein Thorrud, Hai Nguyen, Helge Langseth and Anders-Kofod Petersen
11:20-11:40 An ensemble approach for multi-label classification of item click sequences
by Murat Yagci, Tevfik Aytekin and Fikret Gurgen
11:40-12:00 Predicting User Purchase in E-commerce by Comprehensive Feature Engineering and Decision Boundary Focused Under-Sampling
by Chanyoung Park, Donghyun Kim, Jinoh Oh and Hwanjo Yu
12:00-12:20 Linear and Non-Linear Models for Purchase Prediction
by Wenliang Chen, Zhenghua Li and Min Zhang
12:20-12:40 Multi-Perspective Modeling for Click Event Prediction
by Tzu-Chun Lin and Xia Ning

  • David Ben-Shimon, YooChoose Labs, Israel
  • Michael Friedmann, YooChoose GmbH, Germany
  • Lior Rokach, Ben Gurion University of the Negev, Israel
  • Bracha Shapira, Ben Gurion University of the Negev, Israel

Wednesday, Sept 16, 2015, 09:00-12:30


HS 3

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