Learning to rank relevant and novel documents through user feedback

We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several desirable properties: (i) It measures retrieval performance in terms of relevant as well as novel information, (ii) gives more importance to top ranks to reflect common browsing behavior of users, as opposed to existing objective functions based on set-coverage, (iii) accommodates different levels of tolerance towards redundancy, which is not taken into account by existing evaluation measures, and (iv) extends naturally to the evaluation of session-based retrieval comprising multiple ranked lists. Our ground truth is defined in terms of “information nuggets”, which are obviously not known to the retrieval system when processing a new user query. Therefore, our approach uses observable query and document features (words and named entities) as surrogates for nuggets, whose weights are learned based on user...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Abhimanyu Lad, Yiming Yang
• Keywords: CIKM 2010, Common Browsing Behavior, Information Technology, Objective Function, Performance