TY - GEN
T1 - Time-aware travel attraction recommendation
AU - Wang, Kai
AU - Zhang, Richong
AU - Liu, Xudong
AU - Guo, Xiaohui
AU - Sun, Hailong
AU - Huai, Jinpeng
PY - 2013
Y1 - 2013
N2 - The increasing number of tourists uploaded photos make it possible to discover attractive locations. Existing travel recommendation models make use of the geo-related information to infer possible locations that tourists may be interested in. However, the temporal information, such as the date and time when the photo was taken, associated with these photos are not taken into account by most of existing works. We advocate that this information give us a chance to discover the best visiting time period for each location. In this paper, we exploit a 3-way tensor to integrate context information for tourists visited locations. Based on this model, we propose a time-aware recommendation approach for travel destinations. In addition, a tensor factorization-based approach by maximizing the ranking performance measure is proposed for predicting the possible temporal-spatial correlations for tourists. The experimental results on the real tourists uploaded photos at Flickr.com show that our model outperforms existing approaches in terms of the prediction precision, ranking performance and diversity.
AB - The increasing number of tourists uploaded photos make it possible to discover attractive locations. Existing travel recommendation models make use of the geo-related information to infer possible locations that tourists may be interested in. However, the temporal information, such as the date and time when the photo was taken, associated with these photos are not taken into account by most of existing works. We advocate that this information give us a chance to discover the best visiting time period for each location. In this paper, we exploit a 3-way tensor to integrate context information for tourists visited locations. Based on this model, we propose a time-aware recommendation approach for travel destinations. In addition, a tensor factorization-based approach by maximizing the ranking performance measure is proposed for predicting the possible temporal-spatial correlations for tourists. The experimental results on the real tourists uploaded photos at Flickr.com show that our model outperforms existing approaches in terms of the prediction precision, ranking performance and diversity.
UR - https://www.scopus.com/pages/publications/84887480973
U2 - 10.1007/978-3-642-41230-1_15
DO - 10.1007/978-3-642-41230-1_15
M3 - 会议稿件
AN - SCOPUS:84887480973
SN - 9783642412295
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 188
BT - Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings
PB - Springer Verlag
T2 - 14th International Conference on Web Information Systems Engineering, WISE 2013
Y2 - 13 October 2013 through 15 October 2013
ER -