CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

TL;DR

CAPRI is a new framework for POI recommendation that effectively combines various contextual models and evaluates multiple aspects like diversity and fairness, aiming to set a new industry standard.

Contribution

It introduces a novel integration strategy for context-aware models and an expanded evaluation module for POI recommendation systems.

Findings

Enhanced recommendation accuracy with integrated context models

Improved diversity and fairness in POI suggestions

Open-source implementation for reproducibility

Abstract

Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework.