A Serendipity Model for News Recommendation

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 characteris...

Full description

Saved in:
Bibliographic Details
Published inKI 2015: Advances in Artificial Intelligence pp. 111 - 123
Main Authors Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., Naumann, F.
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Cosine Similarity
News Article
Recommendation List
Recommender System
User Study
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9783319244884
3319244884
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-24489-1_9