Data Design for Personalization: Current Challenges and Emerging Opportunities

Workshop at WSDM-2014
Data Design for Personalization: Current Challenges and Emerging Opportunities
Elizabeth Churchill, Atish Das Sarma

Personalization is central to most Internet experiences. Personalization is a data-driven process, whether the data are explicitly gathered (e.g., by asking people to fill out forms) or implicitly (e.g. through analysis of behavioral data).

It is clear that designing for effective personalization poses interesting engineering and computer science challenges. However, personalization is also a user experience issue. We believe that encouraging dialogue and collaboration between data mining experts, content providers, and user-focused researchers will offer gains in the area of personalization for search and for other domains.

This workshop is part of a larger effort we are developing: D2D: Data to Design - Design to Data.

Our vision is to provide a forum for researchers and practitioners in computer and systems sciences, data sciences, machine learning, information retrieval, interaction and interface design, and human computer interaction to interact.

Our goal is to explore issues surrounding content and presentation personalization across different devices, and to set an agenda for cross-discipline, collaborative engagement.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

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.