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.
2016

Eberhard Lukas, Trattner Christoph

Recommending Sellers to Buyers in Virtual Marketplaces Leveraging Social Information

WWW '16 Companion Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16, Canton of Geneva, 2016

Konferenz
Social information such as stated interests or geographic check-insin social networks has shown to be useful in many recommendertasks recently. Although many successful examples exist, not muchattention has been put on exploring the extent to which social im-pact is useful for the task of recommending sellers to buyers in vir-tual marketplaces. To contribute to this sparse field of research wecollected data of a marketplace and a social network in the virtualworld of Second Life and introduced several social features andsimilarity metrics that we used as input for a user-basedk-nearestneighbor collaborative filtering method. As our results reveal, mostof the types of social information and features which we used areuseful to tackle the problem we defined. Social information suchas joined groups or stated interests are more useful, while otherssuch as places users have been checking in, do not help much forrecommending sellers to buyers. Furthermore, we find that some ofthe features significantly vary in their predictive power over time,while others show more stable behaviors. This research is rele-vant for researchers interested in recommender systems and onlinemarketplace research as well as for engineers interested in featureengineering.
2015

Lacic Emanuel, Kowald Dominik, Eberhard Lukas, Trattner Christoph, Parra Denis, Leandro Marinho

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
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.
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