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Recently, online social networks have emerged that allow people to share their multimedia files, retrieve interesting content, and discover like-minded people. These systems often provide the possibility to annotate the content with tags and ratings. Using a random walk through the social annotation graph, we have combined these annotations into a retrieval model that effectively balances the personal preferences and opinions of like-minded users into a single relevance ranking for either content, tags, or people. We use this model to identify the influence of different annotation methods and system design aspects on common ranking tasks in social content systems. Our results show that a combination of rating and tagging information can improve tasks like search and recommendation. The optimal influence of both sources on the ranking is highly dependent on the retrieval task and system design. Results on content search and tag suggestion indicate that the profile created by a user's annotations can be used effectively to adapt the ranking to personal preferences. The random walk reduces sparsity problems by smoothly integrating indirectly related concepts in the relevance ranking, which is especially valuable for cold-start users or individual tagging systems like YouTube and Flickr.

On many websites users can personally contribute information, ranging from short text messages to photos and videos. Users can see the information contributed by others and respond to it. These social media actively engage their community in the structuring of the collection by making use of collaborative annotation methods. Next to an improved description of the collection, collaborative annotations give insight in the personal preferences of individual users. Through all interactions with the data, users leave traces that can be exploited by the system to learn this preference and personalise social media access for each individual. This thesis contributes to the understanding of social media and collaborative annotation data by studying various data filtering tasks. Different types of collaborative annotations are used to adapt the collection access to the preference of individual users. The deployed data filtering m

We propose a method to predict a user's favourite locations in a city, based on his Flickr geotags in other cities. We define a similarity between the geotag distributions of two users based on a Gaussian kernel convolution. The geotags of the most similar users are then combined to rerank the popular locations in the target city personalised for this user. We show that this method can give personalised travel recommendations for users with a clear preference for a specific type of landmark.

We propose a kernel convolution method to predict similar locations (wormholes) based on human travel behaviour. A scaling parameter can be used to define a set of relevant users to the target location and we show how the geotags of these users can effectively be aggregated to predict a ranking of similar locations. We evaluate results on world and city level using several independent test collections.

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative se

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user’s tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user’s own preference and the opinion of others.

We propose a method to predict a user's favourite locations in a city, based on his Flickr geotags in other cities. We define a similarity between the geotag distributions of two users based on a Gaussian kernel convolution. The geotags of the most similar users are then combined to rerank the popular locations in the target city personalised for this user. We show that this method can give personalised travel recommendations for users with a clear preference for a specific type of landmark.

Recently, online social networks have emerged that allow people to share their multimedia files, retrieve interesting content, and discover like-minded people. These systems often provide the possibility to annotate the content with tags and ratings. Using a random walk through the social annotation graph, we have combined these annotations into a retrieval model that effectively balances the personal preferences and opinions of like-minded users into a single relevance ranking for either content, tags, or people. We use this model to identify the influence of different annotation methods and system design aspects on common ranking tasks in social content systems. Our results show that a combination of rating and tagging information can improve tasks like search and recommendation. The optimal influence of both sources on the ranking is highly dependent on the retrieval task and system design. Results on content search and tag suggestion indicate that the profile created by a user's annotations can be used effectively to adapt the ranking to personal preferences. The random walk reduces sparsity problems by smoothly integrating indirectly related concepts in the relevance ranking, which is especially valuable for cold-start users or individual tagging systems like YouTube and Flickr.

On many websites users can personally contribute information, ranging from short text messages to photos and videos. Users can see the information contributed by others and respond to it. These social media actively engage their community in the structuring of the collection by making use of collaborative annotation methods. Next to an improved description of the collection, collaborative annotations give insight in the personal preferences of individual users. Through all interactions with the data, users leave traces that can be exploited by the system to learn this preference and personalise social media access for each individual. This thesis contributes to the understanding of social media and collaborative annotation data by studying various data filtering tasks. Different types of collaborative annotations are used to adapt the collection access to the preference of individual users. The deployed data filtering methods are used as a means to learn about the factors that contribute to the accessibility of the information in the system. The results in this thesis show that small variations in data type, user interface and other system aspects appear to have large influence on the access possibilities of social media. By increasing the understanding of collaborative annotation data and the aspects that influence this data, this thesis has been able to improve existing data filtering methods and propose new opportunities for effective personalised access to social media.

We propose a kernel convolution method to predict similar locations (wormholes) based on human travel behaviour. A scaling parameter can be used to define a set of relevant users to the target location and we show how the geotags of these users can effectively be aggregated to predict a ranking of similar locations. We evaluate results on world and city level using several independent test collections.

Recommendation systems are important in social networks that allow the injection of user-generated content and let users indicate their preferences towards the content introduced by others. Considering the increase of usage of these collaborative systems, it seems only a matter of time before the current centralized systems will be replaced by decentralized solutions. However, current collaborative filtering systems assume that recommendations can be based on the entire data collection in the network. This work evaluates the performance of user-based collaborative filtering systems when only partial knowledge about the network is available at an end-user’s computer. We propose a utility model that combines three important aspects of network users (similarity, confidence and usefulness) in order to create a semantic overlay network optimized for autonomous content recommendations. We compare different similarity functions on the most common dataset in collaborative filtering and we show the influence of the confidence and usefulness parameters on both dense and sparse data. We find that the commonly used similarity function results in sub-optimal performance when used as updating criterion for locally stored rating profiles. We show that taking into account the level of confidence in the computed similarity can greatly improve recommendation accuracy, especially when a small user neighborhood is selected. Also, conventional methods select many users that cannot contribute to the recommendation, because they have rated too few items. The usefulness parameter that we introduce compensates for this problem, so that even a small local cache in very sparse data provides valuable recommendations.

Recommendation systems are important in social networks that allow the injection of user-generated content and let users indicate their preferences towards the content introduced by others. Considering the increase of usage of these collaborative systems, it seems only a matter of time before the current centralized systems will be replaced by decentralized solutions. However, current collaborative filtering systems assume that recommendations can be based on the entire data collection in the network. This work evaluates the performance of user-based collaborative filtering systems when only partial knowledge about the network is available at an end-user’s computer. We propose a utility model that combines three important aspects of network users (similarity, confidence and usefulness) in order to create a semantic overlay network optimized for autonomous content recommendations. We compare different similarity functions on the most common dataset in collaborative filtering and we show the influence of the confidence and usefulness parameters on both dense and sparse data. We find that the commonly used similarity function results in sub-optimal performance when used as updating criterion for locally stored rating profiles. We show that taking into account the level of confidence in the computed similarity can greatly improve recommendation accuracy, especially when a small user neighborhood is selected. Also, conventional methods select many users that cannot contribute to the recommendation, because they have rated too few items. The usefulness parameter that we introduce compensates for this problem, so that even a small local cache in very sparse data provides valuable recommendations.