Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences
Abstrak
With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.
Penulis (7)
Jingwen Li
Yi Yang
Xu Gong
Jianwu Jiang
Yanling Lu
Jinjin Lu
Shaoshao Xie
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2023
- Bahasa
- en
- Total Sitasi
- 4×
- Sumber Database
- CrossRef
- DOI
- 10.3390/electronics12204199
- Akses
- Open Access ✓