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87 results on '"Lommatzsch A"'

2. A Framework for Analyzing News Images and Building Multimedia-Based Recommender

Author

Lommatzsch, Andreas, Kille, Benjamin, Styp-Rekowski, Kevin, Karl, Max, Pommering, Jan, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lüke, Karl-Heinz, editor, Eichler, Gerald, editor, Erfurth, Christian, editor, and Fahrnberger, Günter, editor

Published
2019
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3. Continuous Evaluation of Large-Scale Information Access Systems: A Case for Living Labs

Author

Hopfgartner, Frank, Balog, Krisztian, Lommatzsch, Andreas, Kelly, Liadh, Kille, Benjamin, Schuth, Anne, Larson, Martha, Zhai, ChengXiang, Series Editor, de Rijke, Maarten, Series Editor, Belkin, Nicholas J., Editorial Board Member, Clarke, Charles, Editorial Board Member, Kelly, Diane, Editorial Board Member, Sebastiani, Fabrizio, Editorial Board Member, Ferro, Nicola, editor, and Peters, Carol, editor

Published
2019
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4. A Next Generation Chatbot-Framework for the Public Administration

Author

Lommatzsch, Andreas, Barbosa, Simone Diniz Junqueira, Series Editor, Chen, Phoebe, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Hodoň, Michal, editor, Eichler, Gerald, editor, Erfurth, Christian, editor, and Fahrnberger, Günter, editor

Published
2018
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5. Towards the Automatic Sentiment Analysis of German News and Forum Documents

Author

Lommatzsch, Andreas, Bütow, Florian, Ploch, Danuta, Albayrak, Sahin, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Eichler, Gerald, editor, Erfurth, Christian, editor, and Fahrnberger, Günter, editor

Published
2017
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6. CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education

Author

Lommatzsch, Andreas, Kille, Benjamin, Hopfgartner, Frank, Larson, Martha, Brodt, Torben, Seiler, Jonas, Özgöbek, Özlem, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jones, Gareth J.F., editor, Lawless, Séamus, editor, Gonzalo, Julio, editor, Kelly, Liadh, editor, Goeuriot, Lorraine, editor, Mandl, Thomas, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2017
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7. A Highly Available Real-Time News Recommender Based on Apache Spark

Author

Domann, Jaschar, Lommatzsch, Andreas, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jones, Gareth J.F., editor, Lawless, Séamus, editor, Gonzalo, Julio, editor, Kelly, Liadh, editor, Goeuriot, Lorraine, editor, Mandl, Thomas, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2017
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9. Topic Tracking in News Streams Using Latent Factor Models

Author

Meiners, Jens, Lommatzsch, Andreas, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Fahrnberger, Günter, editor, Eichler, Gerald, editor, and Erfurth, Christian, editor

Published
2016
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10. Overview of NewsREEL’16: Multi-dimensional Evaluation of Real-Time Stream-Recommendation Algorithms

Author

Kille, Benjamin, Lommatzsch, Andreas, Gebremeskel, Gebrekirstos G., Hopfgartner, Frank, Larson, Martha, Seiler, Jonas, Malagoli, Davide, Serény, András, Brodt, Torben, de Vries, Arjen P., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fuhr, Norbert, editor, Quaresma, Paulo, editor, Gonçalves, Teresa, editor, Larsen, Birger, editor, Balog, Krisztian, editor, Macdonald, Craig, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2016
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16. Stream-Based Recommendations: Online and Offline Evaluation as a Service

Author

Kille, Benjamin, Lommatzsch, Andreas, Turrin, Roberto, Serény, András, Larson, Martha, Brodt, Torben, Seiler, Jonas, Hopfgartner, Frank, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mothe, Josanne, editor, Savoy, Jacques, editor, Kamps, Jaap, editor, Pinel-Sauvagnat, Karen, editor, Jones, Gareth, editor, San Juan, Eric, editor, Capellato, Linda, editor, and Ferro, Nicola, editor

Published
2015
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17. Optimizing and Evaluating Stream-Based News Recommendation Algorithms

Author

Lommatzsch, Andreas, Werner, Sebastian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mothe, Josanne, editor, Savoy, Jacques, editor, Kamps, Jaap, editor, Pinel-Sauvagnat, Karen, editor, Jones, Gareth, editor, San Juan, Eric, editor, Capellato, Linda, editor, and Ferro, Nicola, editor

Published
2015
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18. Benchmarking News Recommendations in a Living Lab

Author

Hopfgartner, Frank, Kille, Benjamin, Lommatzsch, Andreas, Plumbaum, Till, Brodt, Torben, Heintz, Tobias, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kanoulas, Evangelos, editor, Lupu, Mihai, editor, Clough, Paul, editor, Sanderson, Mark, editor, Hall, Mark, editor, Hanbury, Allan, editor, and Toms, Elaine, editor

Published
2014
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19. Real-Time News Recommendation Using Context-Aware Ensembles

Author

Lommatzsch, Andreas, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, de Rijke, Maarten, editor, Kenter, Tom, editor, de Vries, Arjen P., editor, Zhai, ChengXiang, editor, de Jong, Franciska, editor, Radinsky, Kira, editor, and Hofmann, Katja, editor

Published
2014
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26. ECML PKDD 2020 Workshops

Author

Zbigniew W. Ras, João Gama, Elio Masciari, Ricard Gavaldà, Giuseppe Manco, Peter Christen, Iberia Medeiros, Özlem Özgöbek, Jon Atle Gulla, Riccardo Guidotti, Pedro M. Ferreira, Andreas Lommatzsch, Eirini Ntoutsi, Corrado Loglisci, Donato Malerba, Linara Adilova, Arthur Zimek, Annalisa Appice, Michelangelo Ceci, Anna Monreale, Rita P. Ribeiro, Luiza Antonie, Albrecht Zimmermann, Erich Schubert, Przemyslaw Biecek, Yamuna Krishnamurthy, Irena Koprinska, Benjamin Kille, Salvatore Rinzivillo, Michael Kamp, Koprinska, Irena, Kamp, Michael, Appice, Annalisa, Loglisci, Corrado, Antonie, Luiza, Guidotti, Riccardo, Özgöbek, Özlem, Ribeiro, Rita P., Gavaldà, Ricard, Gama, João, Adilova, Linara, Krishnamurthy, Yamuna, Ferreira, Pedro M., Malerba, Donato, Medeiros, Ibéria, Ceci, Michelangelo, Manco, Giuseppe, Masciari, Elio, Ras, Zbigniew W., Christen, Peter, Zimek, Arthur, Monreale, Anna, Biecek, Przemyslaw, Rinzivillo, Salvatore, Kille, Benjamin, Lommatzsch, Andreas, Gulla, Jon Atle, Equipe CODAG - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), and Normandie Université (NU)

Subjects
Engineering, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Knowledge extraction, business.industry, Library science, business, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020.

Published
2020

27. A Framework for Analyzing News Images and Building Multimedia-Based Recommender

Author

Jan Pommering, Andreas Lommatzsch, Kevin Styp-Rekowski, Max Karl, and Benjamin Kille

Subjects
Focus (computing), Multimedia, business.industry, Computer science, 020207 software engineering, 02 engineering and technology, Recommender system, computer.software_genre, Image labeling, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, Web application, 020201 artificial intelligence & image processing, business, computer
Abstract

The number and accessibility of published news items have grown recently. Publishers have developed recommender systems supporting users in finding relevant news. Traditional news recommender systems focus on collaborative filtering and content-based strategies. Unlike texts, multimedia content has received little attention. However, images and other multimedia elements affect how users perceive the news. In this work, we present a system that aggregates text-based, image-based, and user interests-based features to foster recommender systems for news. The system monitors a live stream of news and interactions with them. It applies text analysis and automatic image labeling methods for enriching the news stream. A web application visualizes the collected data and statistics. We show that image features are valuable for developing news recommender systems. The created feature-rich dataset constitutes the basis for developing innovative news recommendation approaches.

Published
2019

28. A Next Generation Chatbot-Framework for the Public Administration

Author

Andreas Lommatzsch

Subjects
Focus (computing), Computer science, Context (language use), 0102 computer and information sciences, 02 engineering and technology, Public administration, computer.software_genre, 01 natural sciences, Chatbot, 010201 computation theory & mathematics, 0202 electrical engineering, electronic engineering, information engineering, Question answering, 020201 artificial intelligence & image processing, Dialog system, computer
Abstract

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 specific scenarios and rather simple questions and commands. These systems cannot readily handle application domains characterized by a large number of relevant facts and complex services (e.g., offered by the public administration).

Published
2018

29. A Highly Available Real-Time News Recommender Based on Apache Spark

Author

Andreas Lommatzsch and Jaschar Domann

Subjects
Information retrieval, 020205 medical informatics, Database, Computer science, Response time, 02 engineering and technology, Recommender system, computer.software_genre, Clef, Set (abstract data type), Task (computing), Distributed algorithm, 020204 information systems, Scalability, Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, computer
Abstract

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 high requirements with respect to response time and scalability. Traditional recommender approaches are optimized for the analysis of static data sets. In news recommendation scenarios, characterized by continuous changes, high volume of messages, and tight time constraints, alternative approaches are needed. In this work we present a highly scalable recommender system optimized for the processing of streams. We evaluate the system in the CLEF NewsREEL challenge. Our system is built on Apache Spark enabling the distributed processing of recommendation requests ensuring the scalability of our approach. The evaluation of the implemented system shows that our approach is suitable for the news recommendation scenario and provides high-quality results while satisfying the tight time constraints.

Published
2017

30. CLEF 2017 NewsREEL Overview: A Stream-Based Recommender Task for Evaluation and Education

Author

Özlem Özgöbek, Frank Hopfgartner, Jonas Seiler, Andreas Lommatzsch, Martha Larson, Torben Brodt, and Benjamin Kille

Subjects
Multimedia, Computer science, 02 engineering and technology, Recommender system, computer.software_genre, Clef, Skill sets, Task (project management), World Wide Web, Living lab, 020204 information systems, Scalability, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, computer
Abstract

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 information on users’ preferences and also rapidly fluctuating item collections. In addition, technical aspects, such as response time and scalability, must be considered. Both algorithmic and technical considerations shape working requirements for real-world recommender systems in businesses. NewsREEL represents a unique opportunity to evaluate recommendation algorithms and for students to experience realistic conditions and to enlarge their skill sets. The NewsREEL Challenge requires participants to conduct data-driven experiments in NewsREEL Replay as well as deploy their best models into NewsREEL Live’s ‘living lab’. This paper presents NewsREEL 2017 and also provides insights into the effectiveness of NewsREEL to support the goals of instructors teaching recommender systems to students. We discuss the experiences of NewsREEL participants as well as those of instructors teaching recommender systems to students, and in this way, we showcase NewsREEL’s ability to support the education of future data scientists.

Published
2017

31. Towards the Automatic Sentiment Analysis of German News and Forum Documents

Author

Andreas Lommatzsch, Sahin Albayrak, Danuta Ploch, and Florian Bütow

Subjects
Information retrieval, Computer science, Process (engineering), InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Sentiment analysis, 02 engineering and technology, Variety (linguistics), ComputingMethodologies_ARTIFICIALINTELLIGENCE, language.human_language, Style (sociolinguistics), Task (project management), German, ComputingMethodologies_PATTERNRECOGNITION, Fully automated, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, language, 020201 artificial intelligence & image processing, InformationSystems_MISCELLANEOUS
Abstract

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 the variety of sentiment indicators. The basis for learning powerful sentiment classifiers are annotated datasets, but for many domains and especially with non-English texts hardly any datasets exist. In order to support the development of sentiment classifiers, we have created two corpora: The first corpus is build based on German news articles. Although news articles should be objective, they often excite subjective emotions. The second corpus consists of annotated messages from a German telecommunication forum. In this paper we describe the process of creating the corpora and discuss our approach for tracing sentiment values, defining clear rules for assigning sentiments scores. Given the corpora we train classifiers that yields good classification results and establish valuable baselines for sentiment analysis. We compare the learned classification strategies and discuss how the approaches can be transferred to new scenarios.

Published
2017

32. Topic Tracking in News Streams Using Latent Factor Models

Author

Jens Meiners and Andreas Lommatzsch

Subjects
Service (systems architecture), Information retrieval, User profile, Data stream mining, Computer science, 02 engineering and technology, Recommender system, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Social media, Tracking (education), Haystack, Factor analysis
Abstract

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 irrelevant data. Text-mining techniques have been developed to extract implicit, hidden information. These techniques analyze big datasets and compute “latent” features based on implicit correlations between documents and events. In this paper we develop a system that applies latent factor models on data streams. Our method allows us detecting the dominant topics and tracking the changes in the relevant topics. In addition, we explain how the extracted knowledge is used for computing recommendations based on trending topics and terms. We evaluate our system on a stream of news messages published on the micro-blogging service Twitter. The evaluation shows that our system efficiently extracts topics and provides valuable insights into the continuously changing news stream helping users quickly identifying the most relevant information as well as current trends.

Published
2016

33. Semantic Movie Recommendations

Author

Andreas Lommatzsch

Subjects
Information retrieval, Computer science, Graph (abstract data type), Recommender system, Popularity
Abstract

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 interesting items by computing the similarity between users and items. The big challenges while creating recommender systems are the sparsity of data (the knowledge about users and items is often limited) and the popularity bias (most recommender algorithms tend to recommend popular items already known to the user). Semantic techniques supporting the graph-based representation of knowledge and the integration of heterogeneous datasets allow us to overcome these problems. The aggregation of knowledge from several different sources enables us to take into account many different aspects while computing recommendations. In addition, semantic recommender systems can provide explanations for suggested items helping the user to understand why an unknown item matches the individual user preferences. In this chapter we discuss the challenges in creating recommender systems and explain semantic approaches for the recommendation domain. We discuss the steps for building a semantic recommender system and present a semantic movie recommender system in detail. The advantages of semantic recommender systems compared to traditional recommender approaches are analyzed.

Published
2015

34. News Recommendation in Real-Time

Author

Benjamin Kille, Torben Brodt, and Andreas Lommatzsch

Subjects
Consumption (economics), World Wide Web, Computer science, Collaborative filtering, Recommender system, Login, Information overload, Task (project management)
Abstract

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 songs to listen to, what movies to watch, and what news article to read. In this chapter, we outline the case of suggesting news articles. This task entails a number of challenges. First, news collections do not remain relevant unlike movies or songs. Users continue to request novel contents. Second, users avoid creating consistent profiles thus reject login procedures. Third, requests arrive in enormous streams. Having short consumption times, users quickly request the next article to read. Handling these challenges requires adaptations to existing recommendation strategies as well as developing novel ones.

Published
2015

35. Personalized Information Access Using Semantic Knowledge

Author

Andreas Lommatzsch and Till Plumbaum

Subjects
World Wide Web, User profile, Information retrieval, Computer science, Collaborative filtering, Information access, Information system, Semantic technology, Semantic Web Stack, User interface, Information overload
Abstract

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 access. Systems that adopt to users’ preferences are called adaptive systems. Based on a user profile containing details about the users’ preferences, the system adapts its content or the user interface to the user. In this chapter, we present a personalized news information system, providing users with entertainment news tailored to their needs. Using semantic technologies, the time to learn user preferences is reduced to a few interactions. We present the system in detail, and present an evaluation showing the benefits coming with the semantic approach.

Published
2015

36. Optimizing and Evaluating Stream-Based News Recommendation Algorithms

Author

Andreas Lommatzsch and Sebastian Werner

Subjects
Set (abstract data type), Focus (computing), Information retrieval, Forcing (recursion theory), Computer science, Web page, Relevance (information retrieval), Recommender system, Algorithm
Abstract

Recommender algorithms are powerful tools helping users to find interesting items in the overwhelming amount available data. Classic recommender algorithms are trained based on a huge set of user-item interactions collected in the past. Since the learning of models is computationally expensive, it is difficult to integrate new knowledge into the recommender models. With the growing importance of social networks, the huge amount of data generated by the real-time web e.g. news portals, micro-blogging services, and the ubiquity of personalized web portals stream-based recommender systems get in the focus of research. In this paper we develop algorithms tailored to the requirements of a web-based news recommendation scenario. The algorithms address the specific challenges of news recommendations, such as a context-dependent relevance of news items and the short item lifecycle forcing the recommender algorithms to continuously adapt to the set of news articles. In addition, the scenario is characterized by a huge amount of messages that must be processed per second and by tight time constraints resulting from the fact that news recommendations should be embedded into webpages without a delay. For evaluating and optimizing the recommender algorithms we implement an evaluation framework, allowing us analyzing and comparing different recommender algorithms in different contexts. We discuss the strength and weaknesses both according to recommendation precision and technical complexity. We show how the evaluation framework enables us finding the optimal recommender algorithm for a specific scenarios and contexts.

Published
2015

37. Real-Time News Recommendation Using Context-Aware Ensembles

Author

Andreas Lommatzsch

Subjects
Information retrieval, Delegation, Computer science, business.industry, media_common.quotation_subject, Context (language use), Recommender system, Time to live, Information overload, World Wide Web, Key (cryptography), Relevance (information retrieval), The Internet, business, media_common
Abstract

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 matching the their individual preferences. News and domain-specific information portals are important knowledge sources on the Web frequently accessed by millions of users. In contrast to product recommender systems, news recommender systems must address additional challenges, e.g. short news article lifecycles, heterogonous user interests, strict time constraints, and context-dependent article relevance. Since news articles have only a short time to live, recommender models have to be continuously adapted, ensuring that the recommendations are always up-to-date, hampering the pre-computations of suggestions. In this paper we present our framework for providing real-time news recommendations. We discuss the implemented algorithms optimized for the news domain and present an approach for estimating the recommender performance. Based on our analysis we implement an agent-based recommender system, aggregation several different recommender strategies. We learn a context-aware delegation strategy, allowing us to select the best recommender algorithm for each request. The evaluation shows that the implemented framework outperforms traditional recommender approaches and allows us to adapt to the specific properties of the considered news portals and recommendation requests.

Published
2014

38. Exploring Thematic Coherence in Fake News

Author

Dogo, Martins Samuel, Deepak P., Jurek-Loughrey, Anna, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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39. Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks

Author

Naretto, Francesca, Pellungrini, Roberto, Nardini, Franco Maria, Giannotti, Fosca, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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40. Monitoring Technoscientific Issues in the News

Author

Cammozzo, Alberto, Di Buccio, Emanuele, Neresini, Federico, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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41. Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data

Author

Lisboa, P. J. G., Ortega-Martorell, S., Jayabalan, M., Olier, I., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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42. On the Coherence of Fake News Articles

Author

Singh, Iknoor, Deepak P., Anoop K., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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43. Media Bias in German News Articles: A Combined Approach

Author

Spinde, Timo, Hamborg, Felix, Gipp, Bela, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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44. Pitch Proposal: Recommenders with a Mission - Assessing Diversity in News Recommendations

Author

Vrijenhoek, Sanne, Helberger, Natali, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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45. An Educational News Dataset for Recommender Systems

Author

Xing, Yujie, Mohallick, Itishree, Gulla, Jon Atle, Özgöbek, Özlem, Zhang, Lemei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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46. Approximate Explanations for Classification of Histopathology Patches

Author

de Sousa, Iam Palatnik, Vellasco, Marley M. B. R., da Silva, Eduardo Costa, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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47. Assessing the Difficulty of Labelling an Instance in Crowdworking

Author

Jambigi, Neetha, Chanda, Tirtha, Unnikrishnan, Vishnu, Spiliopoulou, Myra, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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48. Multi-stakeholder News Recommendation Using Hypergraph Learning

Author

Gharahighehi, Alireza, Vens, Celine, Pliakos, Konstantinos, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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49. Assessing the Uncertainty of the Text Generating Process Using Topic Models

Author

Rieger, Jonas, Jentsch, Carsten, Rahnenführer, Jörg, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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50. What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations

Author

Kuźba, Michał, Biecek, Przemysław, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Kamp, Michael, editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Antonie, Luiza, editor, Zimmermann, Albrecht, editor, Guidotti, Riccardo, editor, Özgöbek, Özlem, editor, Ribeiro, Rita P., editor, Gavaldà, Ricard, editor, Gama, João, editor, Adilova, Linara, editor, Krishnamurthy, Yamuna, editor, Ferreira, Pedro M., editor, Malerba, Donato, editor, Medeiros, Ibéria, editor, Ceci, Michelangelo, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, Ras, Zbigniew W., editor, Christen, Peter, editor, Ntoutsi, Eirini, editor, Schubert, Erich, editor, Zimek, Arthur, editor, Monreale, Anna, editor, Biecek, Przemyslaw, editor, Rinzivillo, Salvatore, editor, Kille, Benjamin, editor, Lommatzsch, Andreas, editor, and Gulla, Jon Atle, editor

Published
2020
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