Recommendation of Multimedia Items by Link Analysis and Collaborative Filtering

Authors

  • Davin Wong Nokia Research Center
  • Ella Bingham HIIT University of Helsinki
  • Saara Hyvönen Fonecta Ltd.

Abstract

We investigate two recommendation approaches suitable for online multimedia sharing services. Our first approach, UserRank, recommends items by global interestingness irrespective of user preferences and is based on the analysis of ownership and evaluation link structure. We also present a personalized interestingness algorithm that combines UserRank with collaborative filtering which enables a single parameter to control the degree of personalization in the recommendations. Our initial results from an informal user study are encouraging.

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Published

2021-09-25

How to Cite

Wong, D., Bingham, E., & Hyvönen, S. (2021). Recommendation of Multimedia Items by Link Analysis and Collaborative Filtering. Proceedings of the International AAAI Conference on Web and Social Media, 2(1), 226-227. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18663