Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Publications

A Serendipity Model For News Recommendation

Maximilian Jenders and Thorben Lindhauer and Gjergji Kasneci and Ralf Krestel and Felix Naumann
In KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings, volume 9324 of Lecture Notes in Computer Science, pages 111-123, 9 2015 Springer.

Abstract:

Recommendation algorithms typically work by suggesting items that are similar to the ones that a user likes, or items that similar users like. We propose a content-based recommendation technique with the focus on serendipity of news recommendations. Serendipitous recommendations have the characteristic of being unexpected yet fortunate and interesting to the user, and thus might yield higher user satisfaction. In our work, we explore the concept of serendipity in the area of news articles and propose a general framework that incorporates the benefits of serendipity- and similarity-based recommendation techniques. An evaluation against other baseline recommendation models is carried out in a user study.

Keywords:

Serendipity, HPI, Recommendation, News

BibTeX file

@inproceedings{jenders2015ki,
author = { Maximilian Jenders and Thorben Lindhauer and Gjergji Kasneci and Ralf Krestel and Felix Naumann },
title = { A Serendipity Model For News Recommendation },
year = { 2015 },
volume = { 9324 },
pages = { 111-123 },
month = { 9 },
abstract = { Recommendation algorithms typically work by suggesting items that are similar to the ones that a user likes, or items that similar users like. We propose a content-based recommendation technique with the focus on serendipity of news recommendations. Serendipitous recommendations have the characteristic of being unexpected yet fortunate and interesting to the user, and thus might yield higher user satisfaction. In our work, we explore the concept of serendipity in the area of news articles and propose a general framework that incorporates the benefits of serendipity- and similarity-based recommendation techniques. An evaluation against other baseline recommendation models is carried out in a user study. },
keywords = { Serendipity, HPI, Recommendation, News },
publisher = { Springer },
series = { Lecture Notes in Computer Science },
booktitle = { KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings },
priority = { 0 }
}

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last change: Fri, 20 Nov 2015 12:00:33 +0100