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Article
Open AccessWhen algorithm selection meets Bi-linear Learning to Rank: accuracy and inference time trade off with candidates expansion
Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine ...
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Chapter
Continuous Evaluation of Large-Scale Information Access Systems: A Case for Living Labs
A/B testing is currently being increasingly adopted for the evaluation of commercial information access systems with a large user base since it provides the advantage of observing the efficiency and effectiven...
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Chapter and Conference Paper
A Framework for Analyzing News Images and Building Multimedia-Based Recommender
The number and accessibility of published news items have grown rec...
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Chapter and Conference Paper
A Next Generation Chatbot-Framework for the Public Administration
With the growing importance of dialog system and personal assistance systems (e.g. Google Now or Amazon Alexa) chatbots arrive more and more in the focus of interest. Current chatbots are typically tailored for s...
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Chapter and Conference Paper
A Highly Available Real-Time News Recommender Based on Apache Spark
Recommending news articles is a challenging task due to the continuous changes in the set of available news articles and the context-dependent preferences of users. In addition, news recommenders must fulfill ...
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Chapter and Conference Paper
Towards the Automatic Sentiment Analysis of German News and Forum Documents
The fully automated sentiment analysis on large text collections is an important task in many applications scenarios. The sentiment analysis is a challenging task due to the domain-specific language style and ...
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Chapter and Conference Paper
CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education
News recommender systems provide users with access to news stories that they find interesting and relevant. As other online, stream-based recommender systems, they face particular challenges, including limited...
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Article
Towards reproducibility in recommender-systems research
Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge o...
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Chapter and Conference Paper
Overview of NewsREEL’16: Multi-dimensional Evaluation of Real-Time Stream-Recommendation Algorithms
Successful news recommendation requires facing the challenges of dynamic item sets, contextual item relevance, and of fulfilling non-functional requirements, such as response time. The CLEF NewsREEL challenge ...
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Chapter and Conference Paper
Topic Tracking in News Streams Using Latent Factor Models
The increasing number of published news articles and messages in social media make it hard for users to find the relevant information and to track interesting topics. Relevant news is hidden in a haystack of i...
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Chapter
News Recommendation in Real-Time
Recommender systems support users facing information overload situations. Typically, such situations arise as users have to choose between an immense number of alternatives. Examples include deciding what song...
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Chapter and Conference Paper
Optimizing and Evaluating Stream-Based News Recommendation Algorithms
Recommender algorithms are powerful tools hel** users to find interesting items in the overwhelming amount available data. Classic recommender algorithms are trained based on a huge set of user-item interact...
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Chapter
Semantic Movie Recommendations
The overwhelming amount of video and audio content makes it difficult for users to find new high-quality content matching the individual preferences. Recommender systems are built to suggest potentially intere...
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Chapter
Personalized Information Access Using Semantic Knowledge
Handling the amount of information on the Web, known as the information overload problem, requires tremendous effort. One approach that relieves the user from this burden is offering personalized information a...
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Chapter and Conference Paper
Stream-Based Recommendations: Online and Offline Evaluation as a Service
Providing high-quality news recommendations is a challenging task because the set of potentially relevant news items changes continuously, the relevance of news highly depends on the context, and there are tig...
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Chapter and Conference Paper
Real-Time News Recommendation Using Context-Aware Ensembles
With the rapidly growing amount of items and news articles on the internet, recommender systems are one of the key technologies to cope with the information overload and to assist users in finding information ...
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Chapter and Conference Paper
Benchmarking News Recommendations in a Living Lab
Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a li...
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Chapter and Conference Paper
SERUM: Collecting Semantic User Behavior for Improved News Recommendations
How can semantic data and semantic technologies be leveraged for personalization and recommendation services? In this paper, we present SERUM (Semantic Recommendations based on large unstructured datasets), a ...
