research-article

Towards a context-sensitive online newspaper

Authors:

Jeremy Jancsary,

Friedrich Neubarth,

Stephanie Schreitter,

Harald TrostAuthors Info & Claims

CaRR '11: Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation

Pages 2 - 9

https://doi.org/10.1145/1961634.1961636

Published: 13 February 2011 Publication History

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Abstract

We give a detailed account of our experiences in implementing a personalized online newspaper that draws---among other hints---on the context of the user. At the algorithmic core of our framework lies a machine learning model that incorporates numerous features of the eligible articles and the user's current situation. Some of the most important design decisions, however, concern the presentation of suggestions, the collection of explicit and implicit feedback, as well as diversity of the recommendations. We present numerical results obtained during the pilot phase of the project that address a number of these concerns and end with a discussion of open questions and future directions.

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Cited By

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Englmeier K(2021)The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of CoronaProcedia Computer Science10.1016/j.procs.2021.01.115181(149-156)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.01.115
Englmeier K(2020)Named Entities and Their Role in Creating Context InformationProcedia Computer Science10.1016/j.procs.2020.09.243176(2069-2076)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.09.243
Englmeier K(2020)User-Centered Detection of Fake News and Misinformation - Design and Prototypical Implementation in the System ContexterHuman Systems Engineering and Design III10.1007/978-3-030-58282-1_1(3-8)Online publication date: 30-Aug-2020
https://doi.org/10.1007/978-3-030-58282-1_1

Index Terms

Towards a context-sensitive online newspaper
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

CaRR '11: Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation

February 2011

57 pages

ISBN:9781450306256

DOI:10.1145/1961634

Conference Chairs:
Ernesto W. De Luca
TU-Berlin, Germany
,
Alan Said
TU-Berlin, Germany
,
Matthias Böhmer
DFKI, Germany
,
Florian Michahelles
ETH, Switzerland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2011

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Österreichische Forschungsförderungsgesellschaft

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CaRR '11

CaRR '11: 2011 Workshop on Context-awareness in Retrieval and Recommendation

February 13, 2011

California, Palo Alto, USA

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Englmeier K(2021)The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of CoronaProcedia Computer Science10.1016/j.procs.2021.01.115181(149-156)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.01.115
Englmeier K(2020)Named Entities and Their Role in Creating Context InformationProcedia Computer Science10.1016/j.procs.2020.09.243176(2069-2076)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.09.243
Englmeier K(2020)User-Centered Detection of Fake News and Misinformation - Design and Prototypical Implementation in the System ContexterHuman Systems Engineering and Design III10.1007/978-3-030-58282-1_1(3-8)Online publication date: 30-Aug-2020
https://doi.org/10.1007/978-3-030-58282-1_1

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Context-Aware Recommendation by Aggregating User Context

Context-Aware Personalization Using Neighborhood-Based Context Similarity

Distributional semantic pre-filtering in context-aware recommender systems