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Trattner Christoph, Oberegger Alexander, Eberhard Lukas, Parra Denis, Marinho Leandro

Understanding the Impact of Weather for POI recomennder systems

RecTour’16,, ACM, Boston, 2016

POI (point of interest) recommender systems for location- based social network services, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated into improving recommenda- tion services in such systems by integrating different features that are assumed to have an impact on people’s preferences for POIs, such as time and geolocation. Yet, little atten- tion has been paid to the impact of weather on the users’ final decision to visit a recommended POI. In this paper we contribute to this area of research by presenting the first results of a study that aims to predict the POIs that users will visit based on weather data. To this end, we extend the state-of-the-art Rank-GeoFM POI recommender algorithm with additional weather-related features, such as tempera- ture, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly increases the recommendation accuracy in comparison to the origi- nal algorithm, but also outperforms its time-based variant. Furthermore, we present the magnitude of impact of each feature on the recommendation quality, showing the need to study the weather context in more detail in the light of POI recommendation systems.
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