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Methods Comparison: Enhancing Diversity for Personalized Recommendation with Practical E-Commerce Data

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(9), pp.59-68
  • DOI : 10.9708/jksci.2022.27.09.059
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 10, 2022
  • Accepted : September 14, 2022
  • Published : September 30, 2022

Juryon Paik 1

1평택대학교

Accredited

ABSTRACT

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users’ preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user’s satisfaction if a recommender system focuses on only the preferences, that is caused by a ‘filter bubble effect’. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.