Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
What's new on the web?: the evolution of the web from a search engine perspective
Proceedings of the 13th international conference on World Wide Web
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Shuffling a stacked deck: the case for partially randomized ranking of search engine results
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
An eye tracking study of the effect of target rank on web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Entropy of search logs: how hard is search? with personalization? with backoff?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
A characterization of online browsing behavior
Proceedings of the 19th international conference on World wide web
Proceedings of the 19th international conference on World wide web
User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
On caption bias in interleaving experiments
Proceedings of the 21st ACM international conference on Information and knowledge management
Beliefs and biases in web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Captions and biases in diagnostic search
ACM Transactions on the Web (TWEB)
Evaluating and predicting user engagement change with degraded search relevance
Proceedings of the 22nd international conference on World Wide Web
Hi-index | 0.00 |
This paper uncovers a new phenomenon in web search that we call domain bias --- a user's propensity to believe that a page is more relevant just because it comes from a particular domain. We provide evidence of the existence of domain bias in click activity as well as in human judgments via a comprehensive collection of experiments. We begin by studying the difference between domains that a search engine surfaces and that users click. Surprisingly, we find that despite changes in the overall distribution of surfaced domains, there has not been a comparable shift in the distribution of clicked domains. Users seem to have learned the landscape of the internet and their click behavior has thus become more predictable over time. Next, we run a blind domain test, akin to a Pepsi/Coke taste test, to determine whether domains can shift a user's opinion of which page is more relevant. We find that domains can actually flip a user's preference about 25% of the time. Finally, we demonstrate the existence of systematic domain preferences, even after factoring out confounding issues such as position bias and relevance, two factors that have been used extensively in past work to explain user behavior. The existence of domain bias has numerous consequences including, for example, the importance of discounting click activity from reputable domains.