Learning to rank for personalized news recommendation
Improving user experience through personalized recommendations is crucial to organizing the abundance of data on news websites. Modeling user preferences based on implicit feedback has recently gained lots of attention, partly due to growing volume of web generated click stream data. Matrix factorization learned with stochastic gradient descent has successfully been adopted to approximate various ranking objectives. The aim of this paper is to test the performance of learning to rank approaches on the real-world dataset and apply some simple heuristics to consider temporal dynamics present in news domain. Our model is based on WARP loss with changes to classic factorization model.