RecSys Challenge 2017: Offline and Online Evaluation

被引:40
作者
Abel, Fabian [1 ]
Deldjoo, Yashar [2 ]
Elahi, Mehdi [3 ]
Kohlsdorf, Daniel [1 ]
机构
[1] XING AG, Hamburg, Germany
[2] Politecn Milan, Milan, Italy
[3] Free Univ Bozen Bolzano, Bozen Bolzano, Italy
来源
PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17) | 2017年
关键词
Recommender Systems; Challenge; Cold Start;
D O I
10.1145/3109859.3109954
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The ACM Recommender Systems Challenge 20171 focused on the problem of job recommendations: given a new job advertisement, the goal was to identify those users who are both (a) interested in getting notified about the job advertisement, and (b) appropriate candidates for the given job. Participating teams had to balance between user interests and requirements for the given job as well as dealing with the cold- start situation. For the first time in the history of the conference, the RecSys challenge offered an online evaluation: teams first had to compete as part of a traditional offline evaluation and the top 25 teams were then invited to evaluate their algorithms in an online setting, where they could submit recommendations to real users. Overall, 262 teams registered for the challenge, 103 teams actively participated and submitted together more than 6100 solutions as part of the offline evaluation. Finally, 18 teams participated and rolled out recommendations to more than 900,000 users on XING(2).
引用
收藏
页码:372 / 373
页数:2
相关论文
共 3 条
[1]   RecSys Challenge 2016: Job Recommendations [J].
Abel, Fabian ;
Benczur, Andras ;
Kohlsdorf, Daniel ;
Larson, Martha ;
Palovics, Robert .
PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), 2016, :425-426
[2]   A survey of active learning in collaborative filtering recommender systems [J].
Elahi, Mehdi ;
Ricci, Francesco ;
Rubens, Neil .
COMPUTER SCIENCE REVIEW, 2016, 20 :29-50
[3]  
Said Alan, 2016, AI MAG, V37, P4