Design and implemenatation of a job recommender system

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.