TY - GEN
T1 - Exploiting hierarchical structures for POI recommendation
AU - Zhao, Pengpeng
AU - Xu, Xiefeng
AU - Liu, Yanchi
AU - Zhou, Ziting
AU - Zheng, Kai
AU - Sheng, Victor S.
AU - Xiong, Hui
N1 - Funding Information:
This research was partially supported by the Natural Science Foundation of China under grant No.71329201, 61502324 and 61532018.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - With the rapid development of location-based social networks, Point-of-Interest (POI) recommendation has played an important role in helping people discover attractive locations. However, existing POI recommendation methods assume a flat structure of POIs, which are better described in a hierarchical structure in reality. Furthermore, we discover that both users' content and spatial preferences exhibit hierarchical structures. To this end, in this paper, we propose a hierarchical geographical matrix factorization model (HGMF) to utilize the hierarchical structures of both users and POIs for POI recommendation. Specifically, we first describe the POI influence degrees over regions with two-dimensional normal distribution, and learn the influence areas of different layers of POIs as the input of HGMF. Then, we perform matrix factorization on user content preference matrix, user spatial preference matrix, and POIs characteristic matrix jointly with the modeling of implicit hierarchical structures. Moreover, a two-step optimization method is proposed to learn the implicit hierarchical structure and find the solution of HGMF efficiently. Finally, we evaluate HGMF on two large-scale real-world location-based social networks datasets. Our experimental results demonstrate that it outperforms the state-of-the-art methods in terms of precision and recall.
AB - With the rapid development of location-based social networks, Point-of-Interest (POI) recommendation has played an important role in helping people discover attractive locations. However, existing POI recommendation methods assume a flat structure of POIs, which are better described in a hierarchical structure in reality. Furthermore, we discover that both users' content and spatial preferences exhibit hierarchical structures. To this end, in this paper, we propose a hierarchical geographical matrix factorization model (HGMF) to utilize the hierarchical structures of both users and POIs for POI recommendation. Specifically, we first describe the POI influence degrees over regions with two-dimensional normal distribution, and learn the influence areas of different layers of POIs as the input of HGMF. Then, we perform matrix factorization on user content preference matrix, user spatial preference matrix, and POIs characteristic matrix jointly with the modeling of implicit hierarchical structures. Moreover, a two-step optimization method is proposed to learn the implicit hierarchical structure and find the solution of HGMF efficiently. Finally, we evaluate HGMF on two large-scale real-world location-based social networks datasets. Our experimental results demonstrate that it outperforms the state-of-the-art methods in terms of precision and recall.
UR - http://www.scopus.com/inward/record.url?scp=85043990773&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2017.75
DO - 10.1109/ICDM.2017.75
M3 - Conference contribution
AN - SCOPUS:85043990773
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 655
EP - 664
BT - Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
A2 - Karypis, George
A2 - Alu, Srinivas
A2 - Raghavan, Vijay
A2 - Wu, Xindong
A2 - Miele, Lucio
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 November 2017 through 21 November 2017
ER -