In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014
A Large Scale Query Logs Analysis for Assessing Personalization Opportunities in E-commerce Sites
Neel Sundaresan, Zitao Liu
Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics
(1) What user information is useful for personalization;
(2) Importance of per-query personalization
(3) Importance of recency in query prediction.
In this paper, we study these problems and provide some preliminary conclusions
The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.
This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.