TY - GEN
T1 - Investigation of Simulated Annealing Cooling Schedule for Mobile Recommendations
AU - Ye, Zeyang
AU - Xiao, Keli
AU - Deng, Yuefan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/29
Y1 - 2016/1/29
N2 - Nowadays, mobile recommendation has become an important research topic in data science. While many researchers focus on developing new applications and designing algorithms for computational efficiency in different business areas, some significant technical problems in classical algorithms are rarely studied under these applications. Simulated annealing (SA), a key approach in solving global optimization problems, is one of them. To this end, this paper aims to investigate the performance of SA in mobile recommendation problems with a focus on identifying the optimal cooling schedule method. We also discuss the move generation, parameter estimation, and the balance between efficiency and effectiveness in SA. Specifically, our tests are based on two problems: a travelling salesman problem and a mobile route recommendation problem. The results suggest that the exponential-based method performs the best to achieve the optimal final energy, while the greedy method, constant-rated-based method, and logarithm-based method are dominant in terms of computational efficiency. Our studies would serve as a guidance of SA for mobile recommendation algorithm designs, especially for the selection of cooling schedule and related parameter estimation.
AB - Nowadays, mobile recommendation has become an important research topic in data science. While many researchers focus on developing new applications and designing algorithms for computational efficiency in different business areas, some significant technical problems in classical algorithms are rarely studied under these applications. Simulated annealing (SA), a key approach in solving global optimization problems, is one of them. To this end, this paper aims to investigate the performance of SA in mobile recommendation problems with a focus on identifying the optimal cooling schedule method. We also discuss the move generation, parameter estimation, and the balance between efficiency and effectiveness in SA. Specifically, our tests are based on two problems: a travelling salesman problem and a mobile route recommendation problem. The results suggest that the exponential-based method performs the best to achieve the optimal final energy, while the greedy method, constant-rated-based method, and logarithm-based method are dominant in terms of computational efficiency. Our studies would serve as a guidance of SA for mobile recommendation algorithm designs, especially for the selection of cooling schedule and related parameter estimation.
KW - cooling schedule
KW - mobile recommendation
KW - route recommendation
KW - simulated annealing
KW - travelling salesman problem
UR - http://www.scopus.com/inward/record.url?scp=84964778798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964778798&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2015.226
DO - 10.1109/ICDMW.2015.226
M3 - Conference contribution
AN - SCOPUS:84964778798
T3 - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
SP - 1078
EP - 1084
BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
A2 - Wu, Xindong
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Dy, Jennifer G.
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Cui, Peng
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Y2 - 14 November 2015 through 17 November 2015
ER -