@inproceedings{d8742f9fdbba47d8ad57193439de6849,
title = "In-house solution for the RecSys challenge 2015",
abstract = "RecSys Challenge 2015 is about predicting the items a user will buy in a given click session. We describe the in-house solution to the challenge as guided by the YOOCHOOSE team. The presented solution achieved 14th place in the challenge's final leaderboard with a score of 51,932 points, while the winner obtained 63,102 points. We suggest two simple and easy to reconstruct approaches for obtaining a prediction in each session. In the first approach we suggest one classifier to determine whether each item in the session will be bought. In the second approach we suggest a two level classification model in which the first level determines whether the session is going to end with a purchase or not, and if it ends with a purchase, the second level classification determines the items that are going to be purchased.",
keywords = "In-house solution, Recommender systems, RecSys challenge 2015",
author = "Nadav Cohen and Bracha Shapira and Adi Gerzi and Lior Rokach and David Ben-Shimon and Michael Friedmann",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; International ACM Recommender Systems Challenge, RecSys 2015 ; Conference date: 16-09-2015",
year = "2015",
month = sep,
day = "16",
doi = "10.1145/2813448.2813519",
language = "American English",
series = "Proceedings of the International ACM Recommender Systems Challenge 2015",
booktitle = "Proceedings of the International ACM Recommender Systems Challenge 2015",
}