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New recommender system evaluation approaches based on user selections factor.

M Kshour1, M Ebrahimi1, S Goliaee1

  • 1Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.

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|July 21, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces improved recommender system (RS) evaluation measures, variety and newness, based on human behavior. These new rules enhance precision and centralization, benefiting RS research and social networking applications.

Keywords:
DiversityEvaluationHuman behaviorNewnessNoveltyRecommender systemVariety

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Area of Science:

  • Computer Science
  • Information Retrieval

Background:

  • Recommender systems (RSs) are crucial in e-commerce and social networking.
  • Existing RS evaluation metrics often lack clarity and are difficult to extend.
  • There is a need for more reliable and human-behavior-aligned RS evaluation measures.

Purpose of the Study:

  • To improve existing recommender system evaluation measures (diversity and novelty) into novel metrics (variety and newness).
  • To develop evaluation rules that better reflect human behavior for increased reliability.
  • To enhance the precision and centralization of recommender system evaluations.

Main Methods:

  • Developed two novel recommender system evaluation measures: variety and newness.
  • Based the new measures on human behavior, adapting previous diversity and novelty rules.
  • Applied the new rules to improve suggestion weighting and user behavior consideration.

Main Results:

  • The new variety and newness measures demonstrated improved precision and centralization.
  • Variety measure showed a 22.54% improvement.
  • Newness measure showed a 14.84% improvement.

Conclusions:

  • The enhanced variety and newness measures offer greater reliability and compatibility for recommender systems.
  • These improved metrics facilitate better comparative analyses and RS development, particularly in social networking.
  • The study aims to foster advancements in recommender system research and competition.