Point of Interest (POI) Recommendation refers to the process of suggesting or recommending specific locations or places of interest to users based on their preferences, historical behavior, and contextual information. POI can include a wide range of locations such as restaurants, landmarks, parks, museums, shopping centers, and more. Applications of POI recommendation systems are common in various domains, including travel, navigation, and location-based services. Mobile apps, social platforms, and online mapping services leverage POI recommendations to enhance user experiences by helping them discover new and relevant places.

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You Are What and Where You Are: Graph Enhanced Attention Network for Explainable POI Recommendation

Point-of-interest (POI) recommendation is an emerging area of research on location-based social networks to analyze user behaviors and contextual check-in information. For this problem, existing approaches, with shallow or deep architectures, have two major drawbacks. First, for these approaches, the attributes of individuals have been largely ignored. Therefore, it would be hard, if not impossible, to gather sufficient user attribute features to have complete coverage of possible motivation factors. Second, most existing models preserve the information of users or POIs by latent representations without explicitly highlighting salient factors or signals. Consequently, the trained models with unjustifiable parameters provide few persuasive rationales to explain why users favor or dislike certain POIs and what really causes a visit. To overcome these drawbacks, we propose GEAPR, a POI recommender that is able to interpret the POI prediction in an end-to-end fashion. Specifically, GEAPR learns user representations by aggregating different factors, such as structural context, neighbor impact, user attributes, and geolocation influence. GEAPR takes advantage of a triple attention mechanism to quantify the influences of different factors for each resulting recommendation and performs a thorough analysis of the model interpretability. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed model. GEAPR is deployed and under test on an internal web server. An example interface is presented to showcase its application on explainable POI recommendation.