Department of Computer Engineering
CS 690 SEMINAR
Learning to Rank with Click Propagation in Educational Search
Computer Engineering Department
Web search is one of the most popular internet activities among users. Due to high usage of search engines, there are huge data available about history of user search issues. Using query logs as a source of implicit feedback, researchers can learn useful patterns about general search behaviors. We employ a detailed query log analysis provided by a commercial educational vertical search engine. Due to difference in terms of search behavior between web users and students, we propose an educational ranking model using learning to rank algorithms to better reflect the search habits of the students in the educational domain to further enhance the search engine performance. We also propose a novel Propagation Algorithm to be used for queries having lower frequencies where information derived from query logs is not enough to exploit. We report that our model constructed using the features generated by our proposed algorithm performs better for singleton queries compared to both the educational learning to rank model we introduce and models learned with common features introduced in the literature.
DATE: 24 October, 2016, Monday @ 16:00