Quantifying the effect of user interface design features on cyberstore traffic and sales
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Empirically validated web page design metrics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of the American Society for Information Science and Technology
Controlled experiments on the web: survey and practical guide
Data Mining and Knowledge Discovery
Combining anchor text categorization and graph analysis for paid link detection
Proceedings of the 18th international conference on World wide web
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Quality-biased ranking of web documents
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to rank with multiple objective functions
Proceedings of the 20th international conference on World wide web
Large-scale validation and analysis of interleaved search evaluation
ACM Transactions on Information Systems (TOIS)
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Modern search engines are good enough to answer popular commercial queries with mainly highly relevant documents. However, our experiments show that users behavior on such relevant commercial sites may differ from one to another web-site with the same relevance label. Thus search engines face the challenge of ranking results that are equally relevant from the perspective of the traditional relevance grading approach. To solve this problem we propose to consider additional facets of relevance, such as trustability, usability, design quality and the quality of service. In order to let a ranking algorithm take these facets in account, we proposed a number of features, capturing the quality of a web page along the proposed dimensions. We aggregated new facets into the single label, commercial relevance, that represents cumulative quality of the site. We extrapolated commercial relevance labels for the entire learning-to-rank dataset and used weighted sum of commercial and topical relevance instead of default relevance labels. For evaluating our method we created new DCG-like metrics and conducted off-line evaluation as well as on-line interleaving experiments demonstrating that a ranking algorithm taking the proposed facets of relevance into account is better aligned with user preferences.