Traditional point of interest (POI) recommendation methods often fail to fully mine the relationships between users and POIs, resulting in limited capacity to capture user preferences. Although graph enhancement method offers improved relational modeling, it is susceptible to noise, which reduces the recommendation precision. To address these challenges, this paper proposes a POI recommendation method named combining universal trajectory maps and multiple preferences. Firstly, a weighted bipartite graph between users and POIs is constructed. Graph convolutional network (GCN) is used to extract the interactive relationship between users and POIs to learn users’ interest preferences. Users are clustered using the obtained interest preferences. The general trajectory maps of the same type of users are built to reduce the impact of noise information. GCN is further used to mine the group features of different types of users and enrich feature representation. Secondly, the group features are combined with time-aware category information and spatio-temporal context from the user’s current trajectory. Transformer model is used to capture deep behavioral preferences. Finally, a non-linear additive function is used to dynamically combine interest preferences with current behavior preferences to fully capture user preferences and generate POI recommendations. The validity of the proposed method is verified on real data sets.
LU Jing
,
GE Cong
. Point of Interest Recommendation Method Combining Universal Trajectory Maps and Multiple Preferences[J]. Journal of Applied Sciences, 2025
, 43(5)
: 771
-784
.
DOI: 10.3969/j.issn.0255-8297.2025.05.005
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