Hidden location prediction using check-in patterns in location-based social networks
| Autor: | Bidyut Kr. Patra, Russell Lock, Pramit Mazumdar, Korra Sathya Babu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Social network
Check-in business.industry Computer science Association (object-oriented programming) 02 engineering and technology Similarity measure Machine learning computer.software_genre Data science Human-Computer Interaction Beijing Ranking Artificial Intelligence Hardware and Architecture 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Suspect business Hidden Markov model computer Software Information Systems |
| Zdroj: | Knowledge and Information Systems. 57:571-601 |
| ISSN: | 0219-3116 0219-1377 |
| Popis: | Check-in facility in a location-based social network (LBSN) enables people to share location information as well as real-life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN. Research into extrapolating unchecked locations from historical data is limited. Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user trajectory. The proposed ALPM explores the association between a user's checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state-of-the-art work in the literature. |
| Databáze: | OpenAIRE |
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