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A method for evaluating discoverability and navigability of recommendation algorithms.

Daniel Lamprecht1, Markus Strohmaier2,3, Denis Helic1

  • 1ISDS, Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria.

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This study introduces a new method to evaluate recommendation algorithms, focusing on how well they help users discover and navigate content. It moves beyond simple metrics to assess multi-click exploration success.

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Area of Science:

  • Information Science
  • Computer Science
  • Human-Computer Interaction

Background:

  • Recommendation systems are crucial for content discovery on platforms like Netflix and YouTube.
  • Existing evaluation metrics inadequately assess the discoverability and navigability of recommendation algorithms.
  • Lack of clear item categorization necessitates better evaluation of user exploration support.

Purpose of the Study:

  • To propose a novel method for evaluating the discoverability and navigability of recommendation algorithms.
  • To expand the existing repertoire of recommendation evaluation techniques.
  • To provide a comprehensive approach for assessing multi-click information seeking scenarios.

Main Methods:

  • Evaluating discoverability by analyzing structural properties (bow tie structure, path lengths) of recommender systems.
  • Assessing navigability through simulations of three distinct information-seeking models.
  • Measuring success rates in simulated user exploration scenarios.

Main Results:

  • Demonstrated feasibility by applying the method to four non-personalized algorithms across three datasets.
  • Illustrated the applicability of the method to personalized recommendation algorithms.
  • Expanded evaluation from one-click to multi-click analysis.

Conclusions:

  • The proposed method offers a general and comprehensive approach to evaluating the navigability of any recommendation algorithm.
  • This work enhances the toolkit for recommendation system evaluation, particularly for user discovery and exploration.
  • The findings support the development of more effective recommendation systems that facilitate deeper content engagement.