Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The 8 requirements of real-time stream processing
ACM SIGMOD Record
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
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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.