When Recommendation Goes Wrong - Anomalous Link Discovery in Recommendation Networks
Venue
Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016) (to appear)
Publication Year
2016
Authors
Bryan Perozzi, Michael Schueppert, Jack Saalweachter, Mayur Thakur
BibTeX
Abstract
We present a secondary ranking system to find and remove erroneous suggestions from
a geospatial recommendation system. We discover such anomalous links by “double
checking” the recommendation system’s output to ensure that it is both structurally
cohesive, and semantically consistent. Our approach is designed for the Google
Related Places Graph, a geographic recommendation system which provides results for
hundreds of millions of queries a day. We model the quality of a recommendation
between two geographic entities as a function of their structure in the Related
Places Graph, and their semantic relationship in the Google Knowledge Graph. To
evaluate our approach, we perform a large scale human evaluation of such an
anomalous link detection system. For the long tail of unpopular entities, our
models can predict the recommendations users will consider poor with up to 42%
higher mean precision (29 raw points) than the live system. Results from our study
reveal that structural and semantic features capture different facets of
relatedness to human judges. We characterize our performance with a qualitative
analysis detailing the categories of real-world anomalies our system is able to
detect, and provide a discussion of additional applications of our method.
