Automatic learning of user interests for personalized communication services

In view of the overwhelming popularity of user generated content new intelligent services are needed to filter this content based on personal interests. In this paper we present a set of algorithms for retrieving content, based on dynamic user profiles and learning capabilities. To illustrate the approach taken, a rich communication service is presented

Georeferencing flickr resources based on textual meta-data

The task of automatically estimating the location of web resources is of central importance in location-based services on the Web. Much attention has been focused on Flickr photos and videos, for which it was found that language modeling approaches are particularly suitable. In particular, state-of-the art systems for georeferencing Flickr photos tend to cluster the locations on Earth in a relatively small set of disjoint regions, apply feature selection to identify location-relevant tags, then use a form of text classification to identify which area is most likely to contain the true location of the resource, and finally attempt to find an appropriate location within the identified area. In this paper, we present a systematic discussion of each of the aforementioned components, based on the lessons we have learned from participating in the 2010 and 2011 editions of MediaEval’s Placing Task. Extensive experimental results allow us to analyze why certain methods work well on this task and show that a median error of just over 1 km can be achieved on a standard benchmark test set

Georeferencing Flickr photos using language models at different levels of granularity: an evidence based approach

The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags has recently gained considerable attention. However, the coordinates that are produced by automated techniques are necessarily variable, since not all resources are described by tags that are sufficiently descriptive. Thus there is a need for adaptive techniques that assign locations to photos at the right level of granularity, or, in some cases, even refrain from making any estimations regarding location at all. To this end, we consider the idea of training language models at different levels of granularity, and combining the evidence provided by these language models using Dempster and Shafer’s theory of evidence. We provide experimental results which clearly confirm that the increased spatial awareness that is thus gained allows us to make better informed decisions, and moreover increases the overall accuracy of the individual language models

Using social media to find places of interest: a case study

In this paper, we show how the large amount of geographically annotated data in social media can be used to complement existing place databases. After explaining our method, we illustrate how this approach can be used to discover new instances of a given semantic type, using London as a case study. In particular, for several place types, our method finds places in London that are not yet contained in the databases used by Foursquare, Google, LinkedGeoData and Geonames. Encouraged by these results, we briefly sketch how similar techniques could potentially be used to identify likely errors in existing databases, to estimate the spatial extent of places, to discover semantic relationships between place types, and to recommend tags to users who are uploading photos