TY - GEN
T1 - Tag2Word
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
AU - Wu, Yong
AU - Yao, Yuan
AU - Xu, Feng
AU - Tong, Hanghang
AU - Lu, Jian
N1 - Funding Information:
This work is supported by the National Basic Research Program of China (No. 2015CB352202) and the National Natural Science Foundation of China (No. 91318301). This work is partially supported by the National Science Foundation under Grant No. IIS1017415, by DTRA under the grant number HDTRA1-16-0017, by Army Research Office under the contract number W911NF-16-1-0168, by National Institutes of Health under the grant number R01LM011986, Region II University Transportation Center under the project number 49997-3325 and a Baidu gift.
Publisher Copyright:
© 2016 ACM.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.
AB - Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.
KW - Generative model
KW - Tag recommendation
KW - Tag-content co-occurrence
UR - http://www.scopus.com/inward/record.url?scp=84996598746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996598746&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983682
DO - 10.1145/2983323.2983682
M3 - Conference contribution
AN - SCOPUS:84996598746
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2287
EP - 2292
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 24 October 2016 through 28 October 2016
ER -