Tagsplanations: Explaining Recommendations Using Tags

Jesse Vig, Shilad Sen, John Riedl, University of Minnesota

Summary

Authors explore the design space of explaining recommendations to user.

Details

One of the research areas explored in improving transparency, trust and user satisfaction in recommender systems is explaining them based on item, user or a certain feature. Authors explore the relevance (relation between item and tag) and preference (relation between user’s sentiments and tag) of tags using explanations called tagsplanations.

For their experiment, authors tested four different interfaces based on different combinations of relevance and preference. Interface that showed relevance and preference while sorting using relevance scored highest in this study pointing that users preferred relevance but did not trust the system enough to view it alone. Both these attributes seemed to be of equal effectiveness.  Subjective tags seemed to perform better than factual tags but if depicting the same idea, users preferred factual tags. For future work, authors propose studying trust and scrutability of such a system.

Review

Authors have presented a strong design space exploration of recommendation explanations and make some insightful observations. In the words of author themselves, it would be desirable to have an empirical measurement of how well the explanations worked instead of using self-reporting.

Disclaimer

The work discussed above is an original work presented at IUI 2009 by the authors/affiliations indicated at the starting of this post. This post in itself was created as part of course requirement of CPSC 436.