Learning to Rank from Relevance Feedback

I will be defending my thesis, Learning to Rank from Relevance Feedback, Monday August 29th at 1500 in room G.005 at Science Park.

Below is a schematic of the method I developed to learn and re-rank documents as users browse through them:

The abstract of my thesis is below. I will post the full text next week.

When searches involve ambiguous terms, require the retrieval of many documents, or are conducted in multiple interactions with the search system, user feedback is especially useful for improving search results. To address these common scenarios we design a search system that uses novel methods to learn from the user's relevance judgements of documents returned for their search. By combining the traditional method of query expansion with learning to rank, our search system uses the interactive nature of search to improve result ordering, even when there are only a small number of judged documents. We present experimental results indicating that our learning to rank method improves result ordering beyond that achievable when using solely query expansion.