Attentional Memory Network with Correlation-based Embedding for time-aware POI recommendation

As considerable amounts of point-of-interest (POI) check-in data have been accumulated, POI recommendation has received much attention recently. It is well recognized that spatial-temporal information plays an important role in the user's decision-making for visiting a POI. However, in time-aware POI recommendation, exploring temporal patterns on user preferences and incorporating multi-view factors for choosing preferred POIs are challenging issues to be resolved. To this end, we propose a novel Attentional Memory Network with Correlation-based Embedding (AMN-CE) for time-aware POI recommendation. Specifically, we first propose a correlation-based POI embedding method to capture geographical influence and interactive correlation between POIs. Sequentially, we design an attentional memory network, which is able to capture the micro-level relationship between time slot pairs. Furthermore, we propose a temporal-level attention mechanism to distinguish and dynamically adjust the influence strength of different time slots on user preferences at the target time slot. The experimental results on four real-life datasets demonstrate significant improvements of our proposed method compared with state-of-the-art models. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a novel time-aware POI recommendation method based on an attentional memory network with correlation-based embedding. By capturing geographical influence and micro-level relationships in time slots, the method has shown significant improvements in recommendation accuracy.