A paradigm of temporal-weather-aware transition pattern for POI recommendation

Abstract

Point of interest (POI) recommendation analyses user preferences through historical check-in data. However, existing POI recommendation methods often overlook the influence of weather information and face the challenge of sparse historical data for individual users. To address these issues, this paper proposes a new paradigm, namely temporal-weather-aware transition pattern for POI recommendation (TWTransNet). This paradigm is designed to capture user transition patterns under different times and weather conditions. Additionally, we introduce the construction of a user-POI interaction graph to alleviate the problem of sparse historical data for individual users. Furthermore, when predicting user interests by aggregating graph information, some POIs may not be suitable for visitation under current weather conditions. To account for this, we propose an attention mechanism to filter POI neighbours when aggregating information from the graph, considering the impact of weather and time. Empirical results on two real-world datasets demonstrate the superior performance of our proposed method, showing a substantial improvement of 6.91%–23.31% in terms of prediction accuracy.