Abstract
From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example , sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the 'sparsity problem,' but is still far from satisfactory. In this paper, an author topic model-based collaborative filtering (ATCF) method is proposed to facilitate comprehensive points of interest (POIs) recommendations for social users. In our approach, user preference topics, such as cultural, cityscape, or landmark, are extracted from the geo-tag constrained textual description of photos via the author topic model instead of only from the geo-tags (GPS locations). Advantages and superior performance of our approach are demonstrated by extensive experiments on a large collection of data.
| Original language | English |
|---|---|
| Article number | 15125522 |
| Pages (from-to) | 907-918 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Multimedia |
| Volume | 17 |
| Issue number | 6 |
| Early online date | 26 Mar 2015 |
| DOIs | |
| Publication status | Published - 1 Jun 2015 |
| Externally published | Yes |
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Author topic model-based collaborative filtering for personalized POI recommendations. / Jiang, Shuhui; Qian, Xueming; Shen, Jialie; Fu, Yun; Mei, Tao.
In: IEEE Transactions on Multimedia, Vol. 17, No. 6, 15125522 , 01.06.2015, p. 907-918.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Author topic model-based collaborative filtering for personalized POI recommendations
AU - Jiang, Shuhui
AU - Qian, Xueming
AU - Shen, Jialie
AU - Fu, Yun
AU - Mei, Tao
PY - 2015/6/1
Y1 - 2015/6/1
N2 - From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example , sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the 'sparsity problem,' but is still far from satisfactory. In this paper, an author topic model-based collaborative filtering (ATCF) method is proposed to facilitate comprehensive points of interest (POIs) recommendations for social users. In our approach, user preference topics, such as cultural, cityscape, or landmark, are extracted from the geo-tag constrained textual description of photos via the author topic model instead of only from the geo-tags (GPS locations). Advantages and superior performance of our approach are demonstrated by extensive experiments on a large collection of data.
AB - From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example , sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the 'sparsity problem,' but is still far from satisfactory. In this paper, an author topic model-based collaborative filtering (ATCF) method is proposed to facilitate comprehensive points of interest (POIs) recommendations for social users. In our approach, user preference topics, such as cultural, cityscape, or landmark, are extracted from the geo-tag constrained textual description of photos via the author topic model instead of only from the geo-tags (GPS locations). Advantages and superior performance of our approach are demonstrated by extensive experiments on a large collection of data.
KW - Data mining
KW - recommendation system
KW - text mining
KW - travel recommendation
UR - http://www.scopus.com/inward/record.url?scp=84929353602&partnerID=8YFLogxK
U2 - 10.1109/TMM.2015.2417506
DO - 10.1109/TMM.2015.2417506
M3 - Article
VL - 17
SP - 907
EP - 918
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
SN - 1520-9210
IS - 6
M1 - 15125522
ER -