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| Content Provider | ACM Digital Library |
|---|---|
| Author | Cui, Yanling Jin, Beihong Ge, Tingjian Zhang, Fusang Ji, Qiang |
| Abstract | The newly emerging location-based social networks (LBSN) such as Tinder and Momo extends social interaction from friends to strangers, providing novel experiences of making new friends. Familiar strangers refer to the strangers who meet frequently in daily life and may share common interests; thus they may be good candidates for friend recommendation. In this paper, we study the problem of discovering familiar strangers, specifically, public transportation trip companions, and their common interests. We collect 5.7 million transaction records of smart cards from about 3.02 million people in the city of Beijing, China. We first analyze this dataset and reveal the temporal and spatial characteristics of passenger encounter behaviors. Then we propose a stability metric to measure hidden friend relations. This metric facilitates us to employ community detection techniques to capture the communities of trip companions. Further, we infer common interests of each community using a topic model, i.e., LDA4HFC (Latent Dirichlet Allocation for Hidden Friend Communities) model. Such topics for communities help to understand how hidden friend clusters are formed. We evaluate our method using large-scale and real-world datasets, consisting of two-week smart card records and 901,855 Point of Interests (POIs) in Beijing. The results show that our method outperforms three baseline methods with higher recommendation accuracy. Moreover, our case study demonstrates that the discovered topics interpret the communities very well. |
| Starting Page | 619 |
| Ending Page | 628 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450340731 |
| DOI | 10.1145/2983323.2983804 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Friend recommendation Familiar strangers Community detection Topic model Location based social network |
| Content Type | Text |
| Resource Type | Article |
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