Reducing Click and Skip Errors in Search Result Ranking

Abstract

Search engines provide result summaries to help users quickly identify whether or not it is worthwhile to click on a result and read in detail. However, users may visit non-relevant results and/or skip relevant ones. These actions are usually harmful to the user experience, but few considered this problem in search result ranking. This paper optimizes relevance of results and user click and skip activities at the same time. Comparing two equally relevant results, our approach learns to rank the one that users are more likely to click on at a higher position. Similarly, it demotes non-relevant web pages with high click probabilities. Experimental results show this approach reduces about 10%-20% of the click and skip errors with a trade off of 2.1% decline in nDCG@10.