Intent-Aware Cross Attention for Next POI Recommendation

Yanling Long, Yanmin Zhu, Mengyuan Jing

Abstract: POI recommendation aims to learn diverse user preferences based on their historical behavioral trajectories and to recommend locations that align with user interests. In this scenario, discerning behavioral patterns and preferences from user check-in sequences plays a crucial role. In recent study, Markov Chain-based methods mainly focus on modeling transition relation between items in a sequence, RNN-based and attention-based methods pay great attention to capture sequence patterns and user’s preferences. However, existing methods neglect the significance of the user’s current intent in next POI recommendation. In our work, we propose a novel Intent-Aware Cross Attention for next POI Recommendation (ICARec) to provide intent insight into the user’s short-term trajectory for better and accurate recommendation. We not only modeling the long/short-term user preference by segmented check-in sequences, but also design an intent cross-attention module which enables interactions between the user’s intent and their most recent check-ins. In addition, to enhance the pairwise independence of intents, we adopt an auxiliary loss function to disentangle intents, endowing them with greater interpretability by ensuring that each intent is represented as an independent and distinct construct within the model. Extensive experiments on two real-world datasets validate the superior performance of our model.