Frequency-based similarity measure for multimedia recommender systems View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-03

AUTHORS

Zia ur Rehman, Farookh K. Hussain, Omar K. Hussain

ABSTRACT

Personalized recommendation has become a pivotal aspect of online marketing and e-commerce as a means of overcoming the information overload problem. There are several recommendation techniques but collaborative recommendation is the most effective and widely used technique. It relies on either item-based or user-based nearest neighborhood algorithms which utilize some kind of similarity measure to assess the similarity between different users or items for generating the recommendations. In this paper, we present a new similarity measure which is based on rating frequency and compare its performance with the current most commonly used similarity measures. The applicability and use of this similarity measure from the perspective of multimedia content recommendation is presented and discussed. More... »

PAGES

95-102

References to SciGraph publications

  • 2007. A Similarity Measure for Collaborative Filtering with Implicit Feedback in ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS. WITH ASPECTS OF ARTIFICIAL INTELLIGENCE
  • 2009. A Survey of Collaborative Filtering Techniques in ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 2009. What Have the Neighbours Ever Done for Us? A Collaborative Filtering Perspective in USER MODELING, ADAPTATION, AND PERSONALIZATION
  • 2010-10-05. A Comprehensive Survey of Neighborhood-based Recommendation Methods in RECOMMENDER SYSTEMS HANDBOOK
  • 2009. Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities in USER MODELING, ADAPTATION, AND PERSONALIZATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00530-012-0281-1

    DOI

    http://dx.doi.org/10.1007/s00530-012-0281-1

    DIMENSIONS

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