Origin-Aware Next Destination Recommendation with Personalized Preference Attention

TL;DR

This paper introduces a novel origin-aware next destination recommendation model that leverages spatial-temporal encoding and personalized attention, significantly improving prediction accuracy in taxi trajectory data.

Contribution

The work presents the STOD-PPA model, which explicitly incorporates origin information and spatial-temporal factors for more accurate personalized destination recommendations.

Findings

Outperforms baseline methods on seven real-world datasets

Effectively models origin-origin, destination-destination, and origin-destination relationships

Demonstrates significant improvements in recommendation accuracy

Abstract

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location. However, recent recommendation works do not satisfy this origin-awareness property, and only consider learning from historical destination locations, without origin information. Thus, the resulting approaches are unable to learn and predict origin-aware recommendations based on the user's current location, leading to sub-optimal performance and poor real-world practicality. Hence, in this work, we study the origin-aware next destination recommendation task. We propose the Spatial-Temporal Origin-Destination Personalized Preference Attention (STOD-PPA) encoder-decoder model to learn origin-origin (OO), destination-destination (DD), and origin-destination (OD) relationships by first encoding both origin and destination sequences with spatial and temporal factors in local and global views, then decoding them through personalized preference attention to predict the next destination. Experimental results on seven real-world user trajectory taxi datasets show that our model significantly outperforms baseline and state-of-the-art methods.