TY - GEN
T1 - Travel recommendation via author topic model based collaborative filtering
AU - Jiang, Shuhui
AU - Qian, Xueming
AU - Shen, Jialie
AU - Mei, Tao
PY - 2015/1/1
Y1 - 2015/1/1
N2 - While automatic travel recommendation has attracted a lot of attentions, the existing approaches generally suffer from different kinds of weaknesses. For example, sparsity problem can significantly degrade the performance of traditional collaborative filtering (CF). If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information. Motivated by this concern, we propose an Author Topic Collaborative Filtering (ATCF) method to facilitate comprehensive Points of Interest (POIs) recommendation for social media users. In our approach, the topics about user preference (e.g., cultural, cityscape, or landmark) are extracted from the textual description of photos by author topic model instead of from GPS (geo-tag). Consequently, unlike CF based approaches, even without GPS records, similar users could still be identified accurately according to the similarity of users' topic preferences. In addition, ATCF doesn’t predefine the category of travel topics. The category and user topic preference could be elicited simultaneously. Experiment results with a large test collection demonstrate various kinds of advantages of our approach.
AB - While automatic travel recommendation has attracted a lot of attentions, the existing approaches generally suffer from different kinds of weaknesses. For example, sparsity problem can significantly degrade the performance of traditional collaborative filtering (CF). If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information. Motivated by this concern, we propose an Author Topic Collaborative Filtering (ATCF) method to facilitate comprehensive Points of Interest (POIs) recommendation for social media users. In our approach, the topics about user preference (e.g., cultural, cityscape, or landmark) are extracted from the textual description of photos by author topic model instead of from GPS (geo-tag). Consequently, unlike CF based approaches, even without GPS records, similar users could still be identified accurately according to the similarity of users' topic preferences. In addition, ATCF doesn’t predefine the category of travel topics. The category and user topic preference could be elicited simultaneously. Experiment results with a large test collection demonstrate various kinds of advantages of our approach.
KW - Author topic model
KW - Multimedia
KW - Travel recommendation
UR - http://www.scopus.com/inward/record.url?scp=84927786233&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14442-9_45
DO - 10.1007/978-3-319-14442-9_45
M3 - Conference Paper published in Proceedings
AN - SCOPUS:84927786233
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 402
BT - MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
PB - Springer-Verlag London Ltd.
CY - Sydney; Australia
T2 - 21st International Conference on MultiMedia Modeling, MMM 2015
Y2 - 5 January 2015 through 7 January 2015
ER -