期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 3, 页码 2014-2027出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2960822
关键词
Object usage event; Social Internet of Things (SIoT); social similarity; time-aware smart object recommendation; user preference
资金
- National Natural Science Foundation of China [61802343]
- Zhejiang Provincial Natural Science Foundation of China [LGF19F020019, LGN20F020003]
- Hangzhou Key Laboratory for IoT Technology and Application
With a large number of possible smart objects in Social Internet of Things (SIoT), a recommendation system is of great necessity to help users find smart objects they need. However, traditional recommendation techniques usually exploit user's rating or feedback information, which are impractical as such kind of user preference information is difficult to collect in the SIoT environment. In addition, temporal context plays an important role in smart object recommendation since most users tend to utilize different objects at different time slots in a day, e.g., making coffee at morning and playing games on weekends. In this article, we propose a time-aware smart object recommendation model by jointly considering user's preference over time and smart object's social similarity. We first learn user's preference over time from his/her object usage events with a latent probabilistic model. Then, we estimate the smart object's social similarity by embedding their heterogeneous social relationships into a shared lower dimensional space. Finally, we generate the recommendation list with an item-based collaborative filtering. We conduct a comprehensive experimental study based on two real-world data sets, and the experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.
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