Global ranking by exploiting user clicks

It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which records the sequence of documents being clicked and not clicked in the result set during a user search session. We formulate the problem as a global ranking problem, emphasizing the importance of the sequential nature of user clicks, with the goal to predict the relevance labels of all the documents in a search session. This is distinct from conventional learning to rank methods that usually design a ranking model defined on a single document; in contrast, in our model the relational information among the documents as manifested by an aggregation of user clicks is exploited to rank all the documents jointly. In particular, we adapt several sequential supervised learning algorithms, including the...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Shihao Ji, Ke Zhou, Ciya Liao, Zhaohui Zheng, Gui-Rong Xue, Olivier Chapelle, Gordon Sun, Hongyuan Zha
• Keywords: Global Ranking Problem, Information Retrieval, SIGIR 2009, User Clicks, User Interactions