TY - GEN
T1 - A Point-of-Interest Recommendation Method Using Location Similarity
AU - Zeng, Jun
AU - Li, Yinghua
AU - Li, Feng
AU - Wen, Junhao
AU - Hirokawa, Sachio
N1 - Funding Information:
This research is supported by Supported by the National Natural Science Foundation of China (Grant No. 61502062 and Grant No. 61672117), the China Postdoctoral Science Foundation under Grant 2014M560704, the Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry), the Fundamental Research Funds for the Central Universities Project No. 2015CDJXY.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/15
Y1 - 2017/11/15
N2 - POI recommendation aims to recommend places which users have not visited before. In this paper, we proposed a POI recommendation method using location similarity, which assumes that people may be interested in the places that are similar with the places that they have been to before. In order to calculate the similarity of locations, we proposed a novel method using time slots. Every two hours can be considered as a time slot. In other words, one day can be segmented into 12 time slots. For each location, the check-in times in each time slot can be collected. These check-in times can form a vector, which can be used to calculate the similarity of two locations. According to the similarity, the score of each unvisited locations can be calculated and sorted. Finally, the POI recommendation can be generated from the top-n unvisited locations. The experiment results show that the proposed method is effective.
AB - POI recommendation aims to recommend places which users have not visited before. In this paper, we proposed a POI recommendation method using location similarity, which assumes that people may be interested in the places that are similar with the places that they have been to before. In order to calculate the similarity of locations, we proposed a novel method using time slots. Every two hours can be considered as a time slot. In other words, one day can be segmented into 12 time slots. For each location, the check-in times in each time slot can be collected. These check-in times can form a vector, which can be used to calculate the similarity of two locations. According to the similarity, the score of each unvisited locations can be calculated and sorted. Finally, the POI recommendation can be generated from the top-n unvisited locations. The experiment results show that the proposed method is effective.
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U2 - 10.1109/IIAI-AAI.2017.122
DO - 10.1109/IIAI-AAI.2017.122
M3 - Conference contribution
AN - SCOPUS:85040568125
T3 - Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
SP - 436
EP - 440
BT - Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
A2 - Hashimoto, Kiyota
A2 - Fukuta, Naoki
A2 - Matsuo, Tokuro
A2 - Hirokawa, Sachio
A2 - Mori, Masao
A2 - Mori, Masao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
Y2 - 9 July 2017
ER -