Combining Heterogenous Social and Geographical Information for Event Recommendation

By Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo, Binxing Fang. 2014

82 citations recorded by the source corpus.

With the rapid growth of event-based social networks (EBSNs) like Meetup, the demand for event recommendation becomes increasingly urgent. In EBSNs, event recommendation plays a central role in recommending the most relevant events to users who are likely to participate in. Different from traditional recommendation problems, event recommendation encounters three new types of information, i.e., heterogenous online+offline social relationships, geographical features of events and implicit rating data from users. Yet combining the three types of data for offline event recommendation has not been considered. Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation. Experimental results on real-world data sets show the performance of our method.

Subfields: Information Systems, Transportation, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Julian McAuley
2. Ruining He
3. Yongfeng Zhang
4. Xiangnan He
5. Xu Chen
6. Philip S. Yu
7. Jiacheng Li
8. Xue Liu

In Transportation

1. Julian McAuley
2. Yongfeng Zhang
3. Philip S. Yu
4. Lei Shu
5. Hu Xu
6. Xu Chen
7. B. Liu
8. Zheng Qin

In Signal Processing

1. Ruining He
2. Julian McAuley

Citations in the corpus

• VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “Others have developed content-based and hybrid models that make use of a variety of information sources, including text (and context), taxonomies, and user demographics (Bao, Fang, and Zhang, 2014; Kanagal et al., 2012; Lu et al., 2015; Qiao et al., 2014).”