improving web search ranking by incorporating user behavior information
We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31\% relative to the original performance.
web search ranking web search ranking user behavior informal data order feedback process exploration user feedback feature evaluation queries user interaction web search engine implicit feedback accuracy competition algorithm performance implicit relevance feedback large-scale relation