Similarity for News Recommender Systems

Nava Tintarev, Judith Masthoff

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


The accuracy of content-based recommender systems tends to depend on the way similarity is defined. In this paper, we will explore different ways to measure similarity for a news recommender system based on news headlines. We will compare human judgements of similarity with Lin’s taxonomy-based measure and the WASP measure that uses annotated corpus data. The main aim of this work is to better understand similarity, so that it can be used to explain recommendations to users.
Original languageEnglish
Title of host publicationWorkshop on Recommender Systems and Intelligent User Interfaces
Subtitle of host publication2nd international workshop on web personalisation,recommender iystems and intelligent iser interfaces
EditorsGulden Uchyigit
Publication statusPublished - 2006

Bibliographical note

In conjunction with the International Conference on Adaptive Hypermedia and
Adaptive Web-Based Systems, AH 2006, Dublin, Ireland, June 20-23, 2006


  • similarity
  • recommender systems


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