Enlister: baidu's recommender system for the biggest chinese Q&A website.

In this paper, we describe the concept & design of a real-time question RS (recommender system), the Enlister project, for the biggest Chinese Q&A (Questions and Answers) website and evaluate its performance on massive data from this real-world practice. We demonstrate how we weigh in among different recommendation algorithms and optimization methods. To enhance recommendation accuracy and handling time-sensitive questions, we propose a large scale real-time RS based on the combination of machine learning algorithms and the stream computing technology. Considering of algorithm flexibility and performance, we use the maximum entropy model as the fundamental model design in the CTR (click-through rate) prediction of recommendation items. In the perspective of the Enlister system architecture, we illustrate how we divide and conquer massive data processing problem with a novel stream computing design which reduces the data process latency down to seconds. Finally we analyze the online test result and prove our design concept by achieving a series of significant improvements.