%0 Conference Paper %1 Gao:2016:CSG:2983323.2983879 %A Gao, Li %A Wu, Jia %A Qiao, Zhi %A Zhou, Chuan %A Yang, Hong %A Hu, Yue %B Proceedings of the 25th ACM International on Conference on Information and Knowledge Management %C New York, NY, USA %D 2016 %I ACM %K event-recommendation social-influence social-information-access %P 1941--1944 %R 10.1145/2983323.2983879 %T Collaborative Social Group Influence for Event Recommendation %U http://doi.acm.org/10.1145/2983323.2983879 %X In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method. %@ 978-1-4503-4073-1

@inproceedings{Gao:2016:CSG:2983323.2983879, abstract = {In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.}, acmid = {2983879}, added-at = {2017-02-08T21:29:07.000+0100}, address = {New York, NY, USA}, author = {Gao, Li and Wu, Jia and Qiao, Zhi and Zhou, Chuan and Yang, Hong and Hu, Yue}, biburl = {https://www.bibsonomy.org/bibtex/21f80b4c8cd3c50e08ec4884c75b36950/xianteng}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, description = {Collaborative Social Group Influence for Event Recommendation}, doi = {10.1145/2983323.2983879}, interhash = {e4f238ce6d8bc7fa4398bac09576b30e}, intrahash = {1f80b4c8cd3c50e08ec4884c75b36950}, isbn = {978-1-4503-4073-1}, keywords = {event-recommendation social-influence social-information-access}, location = {Indianapolis, Indiana, USA}, numpages = {4}, pages = {1941--1944}, publisher = {ACM}, series = {CIKM '16}, timestamp = {2017-02-08T21:29:07.000+0100}, title = {Collaborative Social Group Influence for Event Recommendation}, url = {http://doi.acm.org/10.1145/2983323.2983879}, year = 2016 }