Trajectory-Matching Prediction for Friend Recommendation in Anonymous Social Networks
| Autor: | Wei Yan, Yuanyuan Zhang, Kaigui Bian, Yuanxing Zhang, Yichun Duan, Meng Tong, Chengliang Gao |
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| Rok vydání: | 2017 |
| Předmět: |
Matching (statistics)
Social network Serendipity business.industry Computer science 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Similarity (network science) 020204 information systems 0202 electrical engineering electronic engineering information engineering Trajectory Artificial intelligence business computer |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/glocom.2017.8255086 |
| Popis: | People connect to each other over conventional online social networks (OSNs) based on many parameters (common interests, experiences, locations), while the anonymous social networks (ASNs) recommend candidate friends to a user mainly by the location proximity at a coarse-granularity. In this paper, we formulate a fine-grained trajectory-matching prediction problem for friend recommendation in ASNs--what is the likelihood for two users to encounter with each other in the future based on their historical trajectory data? We define the serendipity of two trajectories in both spatial and temporal domains to quantify the similarity between two users' trajectories, and propose an algorithm that recommends candidate friends to a user by determining the similarity of their trajectories. Experiments show that our proposed algorithm can predict the encounter of users quite accurately and it outperforms the conventional algorithms in terms of the precision and consumed time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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