TY - GEN
T1 - Collective Mobile Sequential Recommendation
T2 - 31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2019)
AU - Wu, Tongwen
AU - Zhang, Zizhen
AU - Li, Yanzhi
AU - Wang, Jiahai
PY - 2019/11
Y1 - 2019/11
N2 - Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multi-taxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes time-varying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In trace-driven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.
AB - Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multi-taxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes time-varying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In trace-driven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.
KW - Planning Algorithms
KW - Planning under Uncertainty
KW - Recommender System
UR - http://www.scopus.com/inward/record.url?scp=85081080034&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85081080034&origin=recordpage
U2 - 10.1109/ICTAI.2019.00-92
DO - 10.1109/ICTAI.2019.00-92
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781728137988
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 1260
EP - 1264
BT - Proceedings - IEEE 31st International Conference on Tools with Artificial Intelligence
PB - IEEE Computer Society
Y2 - 4 November 2019 through 6 November 2019
ER -