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Applying Simulated Annealing and Parallel Computing to the Mobile Sequential Recommendation - 2019

Applying Simulated Annealing And Parallel Computing To The Mobile Sequential Recommendation

Research Area:  Machine Learning


We speed up the solution of the mobile sequential recommendation (MSR) problem that requires searching optimal routes for empty taxi cabs through mining massive taxi GPS data. We develop new methods that combine parallel computing and the simulated annealing with novel global and local searches. While existing approaches usually involve costly offline algorithms and methodical pruning of the search space, our new methods provide direct real-time search for the optimal route without the offline preprocessing. Our methods significantly reduce computational time for the high dimensional MSR problems from days to seconds based on the real-world data as well as the synthetic ones. We efficiently provide solutions to MSR problems with thousands of pick-up points without offline training, compared to the published record of 25 pick-up points.


Author(s) Name:  Zeyang Ye; Keli Xiao; Yong Ge and Yuefan Deng

Journal name:  IEEE Transactions on Knowledge and Data Engineering

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TKDE.2018.2827047

Volume Information:  Volume: 31, Issue: 2, Feb. 1 2019,Page(s): 243 - 256