Predicting Feature-based Similarity in the News Domain Using Human Judgments

A.D. Starke*, Sebastian Øverhaug Larsen, Christoph Trattner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

Abstract

When reading an online news article, users are typically presented ‘more like this’ recommendations by news websites. In this study, we assessed different similarity functions for news item retrieval, by comparing them to human judgments of similarity. We asked 401 participants to assess the overall similarity of ten pairs of political news articles, which were compared to feature-specific similarity functions (e.g., based on body text or images). We found that users indicated to mostly use text-based features (e.g., title) for their similarity judgments, suggesting that body text similarity was the most representative for their judgment. Moreover, we modeled similarity judgments using different regression techniques. Using data from another study, we contrasted our results across retrieval domains, revealing that similarity functions in news are less representative of user judgments than those in movies and recipes.
Original languageEnglish
Title of host publicationProceedings of the 9th International Workshop on News Recommendation and Analytics (INRA 2021) co-located with 15th ACM Conference on Recommender Systems (RecSys 2021)
EditorsÖ. Özgöbek , A. Lommatzsch , B. Kille, P. Liu, Z. Pu, J.A. Gulla
PublisherCEUR Workshop Proceedings (CEUR-WS.org)
Number of pages18
Volume3143
Publication statusPublished - Sept 2021
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands
Duration: 27 Sept 20211 Oct 2021

Publication series

NameCEUR Workshop Proceedings
PublisherRheinisch-Westfaelische Technische Hochschule Aachen
ISSN (Print)1613-0073

Conference/symposium

Conference/symposium15th ACM Conference on Recommender Systems, RecSys 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period27/09/211/10/21

Keywords

  • news
  • similarity
  • similar-item retrieval
  • recommender systems
  • user study
  • human judgment

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