Mokbel’s Group Earns 2022 ACM SIGSPATIAL 10-Year Impact Award
Mohamed Mokbel headshot horizontal

Professor Mohamed Mokbel’s research group was awarded the 2022 Association for Computing Machinery (ACM) Special Interest Group on Spatial Information (SIGSPATIAL) 10-Year Impact Award for their 2012 paper, “Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data.” The annual award recognizes the paper that has made the largest impact in the field over the last 10 years.

University of Minnesota alumni Jia Bao (Ph.D., 2014) was the first author of the paper and collaborated with Yu Zheng, a Microsoft researcher at the time. This is the second 10-year award bestowed to Mokbel’s research group who also earned the prestigious recognition from the Very Large Data Bases (VLDB) Endowment in 2016 for their 2006 paper, “The New Casper: Query Processing for Location Services Without Compromising Privacy.” 
 

Bao, Zheng and Mokbel began working on location-based recommendations in 2010, the year the world was introduced to the iPad and Instagram. Their work focused on providing recommendations for places to visit based on personal preference and current location. 

“There is a ton of data about your preferences in the place you live, but when you visit somewhere new, there is no history to work from,” Mokbel said. “Can we take the data we know about your life in Minneapolis and make appropriate recommendations when you visit someplace new? Based on the user’s data, the program can find people with similar patterns in the new area and make recommendations based on those similarities. It was ahead of its time because it was the first project to focus on both of these factors.” 

After graduating in 2014, Bao was recruited by Zheng to continue their work at Microsoft Research. The pair continue to work together at JD.COM in China where Zheng is the vice president and chief data scientist, and Bao leads the data management department at JD Intelligent City Business Unit. Mokbel continues to work on systems and machine learning techniques for big spatial data and applications, including the interaction of GIS and location-based services with database systems and cloud computing.
 

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