Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2016

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

Understanding the Impact of Weather for POI recomennder systems

RecTour’16,, ACM, Boston, 2016

Konferenz
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.
2015

Trattner Christoph, Balby Marinho Leandro, Parra Denis

Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?

Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015

Konferenz
Large scale virtual worlds such as massive multiplayer online gamesor 3D worlds gained tremendous popularity over the past few years.With the large and ever increasing amount of content available, virtualworld users face the information overload problem. To tacklethis issue, game-designers usually deploy recommendation serviceswith the aim of making the virtual world a more joyful environmentto be connected at. In this context, we present in this paper the resultsof a project that aims at understanding the mobility patternsof virtual world users in order to derive place recommenders forhelping them to explore content more efficiently. Our study focuson the virtual world SecondLife, one of the largest and mostprominent in recent years. Since SecondLife is comparable to realworldLocation-based Social Networks (LBSNs), i.e., users canboth check-in and share visited virtual places, a natural approach isto assume that place recommenders that are known to work well onreal-world LBSNs will also work well on SecondLife. We have putthis assumption to the test and found out that (i) while collaborativefiltering algorithms have compatible performances in both environments,(ii) existing place recommenders based on geographicmetadata are not useful in SecondLife.
2015

Trattner Christoph, Parra Denis, Brusilovsky Peter, Marinho Leandro B.

Report on the SIGIR 2015 Workshop on Social Personalization and Search

SIGIR FORUM, ACM, 2015

Konferenz
The use of contexts –side information associated to information tasks– has been one ofthe most important dimensions for the improvement of Information Retrieval tasks, helpingto clarify the information needs of the users which usually start from a few keywords in atext box. Particularly, the social context has been leveraged in search and personalizationsince the inception of the Social Web, but even today we find new scenarios of informationfiltering, search, recommendation and personalization where the use of social signals canproduce a steep improvement. In addition, the action of searching has become a social processon the Web, making traditional assumptions of relevance obsolete and requiring newparadigms for matching the most useful resources that solve information needs. This escenariohas motivated us for organizing the Social Personalization and Search (SPS) workshop,a forum aimed at sharing and discussing research that leverage social data for improvingclassic personalization models for information access and to revisiting search from individualphenomena to a collaborative process.
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