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- W4387496021 abstract "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." @default.
- W4387496021 created "2023-10-11" @default.
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- W4387496021 date "2023-10-10" @default.
- W4387496021 modified "2023-10-12" @default.
- W4387496021 title "Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences" @default.
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- W4387496021 doi "https://doi.org/10.3390/electronics12204199" @default.
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