Active Learning for Technology Enhanced Learning
AbstractSuggesting tasks and learning resources of appropriate difficulty to learners is challenging. Neither should they be too difficult and nor too easy. Well-chosen tasks would enable a quick...
View ArticleFusion of Similarity Measures for Time Series Classification
AbstractTime series classification, due to its applications in various domains, is one of the most important data-driven decision tasks of artificial intelligence. Recent results show that the simple...
View ArticleNonlinear dimensionality reduction for efficient and effective audio...
AbstractIn this paper, we address the issue of nonlinear dimensionality reduction to efficiently index spectral audio similarity measures. We propose the embedding of the spectral similarity space to a...
View ArticleRFID-Enhanced Museum for Interactive Experience
AbstractVisitors to physical museums are often overwhelmed by the vast amount of information available in the space they are exploring, making it difficult to select personally interesting content....
View ArticleInvariant Time-Series Classification
AbstractTime-series classification is a field of machine learning that has attracted considerable focus during the recent decades. The large number of time-series application areas ranges from medical...
View ArticleAdvanced Techniques
AbstractIn this chapter we describe the state-of-the-art in social tagging recommender systems. Many of the algorithms presented here borrow ideas and techniques from other areas such as information...
View ArticleOnline Evaluation
AbstractThe multiplexing tag recommender of BibSonomy allows for comparisons of different tag recommenders in a realistic real-life setting. We show in this chapter, which kind of evaluation the...
View ArticleOffline Evaluation
AbstractIn this chapter we present the most usual experimental protocols and metrics employed for offline evaluation of tag recommender systems. By offline we mean that the algorithms are evaluated on...
View ArticleBaseline Techniques
AbstractIn this chapter we introduce the most basic techniques for recommendations in STS. Despite their simplicity, these methods are very easy to implement, cheap to compute, and have proven to...
View ArticleSocial Tagging Systems
AbstractSocial Tagging Systems (STS for short) are web applications where users can upload, tag, and share resources (e. g., websites, videos,photos, etc.) with other users. STS promote...
View ArticleIndividualized Error Estimation for Classification and Regression Models
AbstractEstimating the error of classification and regression models is one of the most crucial tasks in machine learning. While the global error is capable to measure the quality of a model, local...
View ArticleConclusions
AbstractIn this chapter we close the book with a summary, a discussion, and future research directions.
View ArticleReal World Social Tagging Recommender Systems
AbstractAs an exemplary implementation of a recommender system for social tagging systems we present in this chapter the tag recommendation framework of BibSonomy. It allows to test, evaluate and...
View ArticleRecommender Systems
AbstractIn the following we will describe systematically and formally the most important problems related to recommender systems and give some references to actual solutions. Our focus here is to...
View ArticleLink injection for boosting information spread in social networks
AbstractSocial media have become popular platforms for spreading information. Several applications, such as ‘viral marketing’, pause the requirement for attaining large-scale information spread in the...
View ArticleP2P RVM for Distributed Classification
AbstractIn recent years there is an increasing interest for analytical methods that learn patterns over large-scale data distributed over Peer-to-Peer (P2P) networks and support applications. Mining...
View ArticleA supervised active learning framework for recommender systems based on...
AbstractA key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to ask new users to rate a few items to reveal their preferences and to use active...
View ArticleRecommender systems in e-learning environments: a survey of the...
AbstractWith the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities...
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