Wir machen unsere Kompetenzen und Innovationen
nach Außen hin sichtbar.

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 featuresthat are assumed to have an impact on people’s preferencesfor 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 wecontribute to this area of research by presenting the firstresults of a study that aims to predict the POIs that userswill visit based on weather data. To this end, we extend thestate-of-the-art Rank-GeoFM POI recommender algorithmwith additional weather-related features, such as tempera-ture, cloud cover, humidity and precipitation intensity. Weshow that using weather data not only significantly increasesthe recommendation accuracy in comparison to the origi-nal algorithm, but also outperforms its time-based variant.Furthermore, we present the magnitude of impact of eachfeature on the recommendation quality, showing the need tostudy the weather context in more detail in the light of POIrecommendation systems.

Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users’ interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.

In this paper, we present work-in-progress of a recently startedproject that aims at studying the effect of time in recommendersystems in the context of social tagging. Despite the existence ofprevious work in this area, no research has yet made an extensiveevaluation and comparison of time-aware recommendation methods.With this motivation, this paper presents results of a studywhere we focused on understanding (i) “when” to use the temporalinformation into traditional collaborative filtering (CF) algorithms,and (ii) “how” to weight the similarity between users and itemsby exploring the effect of different time-decay functions. As theresults of our extensive evaluation conducted over five social taggingsystems (Delicious, BibSonomy, CiteULike, MovieLens, andLast.fm) suggest, the step (when) in which time is incorporated inthe CF algorithm has substantial effect on accuracy, and the typeof time-decay function (how) plays a role on accuracy and coveragemostly under pre-filtering on user-based CF, while item-basedshows stronger stability over the experimental conditions.

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.

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.

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physicalsetting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter.In this paper we analyze scholars’ Twitter usage in 16 Computer Science conferences over a timespan of five years. Ourprimary finding is that over the years there are differences with respect to the uses of Twitter, with an increase ofinformational activity (retweets and URLs), and a decrease of conversational usage (replies and mentions), which alsoimpacts the network structure – meaning the amount of connected components – of the informational and conversationalnetworks. We also applied topic modeling over the tweets’ content and found that when clustering conferences accordingto their topics the resulting dendrogram clearly reveals the similarities and differences of the actual research interests ofthose events. Furthermore, we also analyzed the sentiment of tweets and found persistent differences among conferences.It also shows that some communities consistently express messages with higher levels of emotions while others do it in amore neutral manner. Finally, we investigated some features that can help predict future user participation in the onlineTwitter conference activity. By casting the problem as a classification task, we created a model that identifies factors thatcontribute to the continuing user participation. Our results have implications for research communities to implementstrategies for continuous and active participation among members. Moreover, our work reveals the potential for the useof information shared on Twitter in order to facilitate communication and cooperation among research communities, byproviding visibility to new resources or researchers from relevant but often little known research communities.