@inproceedings{868259ef87b341f9ae426dec2886c94b,
title = "Kariyer.net i{\c c}in İ{\c s} İlan {\"O}neri Sistemi Tasarmi ve Ger{\c c}ekle{\c s}tirimi",
abstract = "Recommender systems help people to find items of interest by utilizing past user interactions (such as product views, ratings, and purchases). Today many e-commerce sites and large scale web applications use recommender systems and provide their customers personalized products. In this work we will share our recent experience in developing a job recommender system based on collaborative filtering at Kariyer.net. In particular, we will explain how and why we choose the recommender algorithm developed in the system, methods for evaluating success, and the system architecture. We will also mention future work that we plan to pursue for solving the problems we face in practice after this successful first attempt.",
keywords = "Collaborative filtering, Job recommendation, Recommendation systems",
author = "Kara, {Kemal Can} and Samet Esen and Ne{\c s}e Kahyalar and Karaka{\c s}, {A. A{\c s}kn} and Tevfik Aytekin",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd International Conference on Computer Science and Engineering, UBMK 2017 ; Conference date: 05-10-2017 Through 08-10-2017",
year = "2017",
month = oct,
day = "31",
doi = "10.1109/UBMK.2017.8093515",
language = "T{\"u}rk{\c c}e",
series = "2nd International Conference on Computer Science and Engineering, UBMK 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "729--733",
booktitle = "2nd International Conference on Computer Science and Engineering, UBMK 2017",
}