Diversity Measurement of Recommender Systems under Different User Choice Models


  • Zoltán Szlávik VU University Amsterdam
  • Wojtek Kowalczyk VU University Amsterdam
  • Martijn Schut VU University Amsterdam


Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them - neither on the supply (company) nor demand (consumer) side. In this paper, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models.We consider a number of different scenarios (which differ in the agent's choice model), run very extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The choice models are an essential part of our work, since these can be influenced by the owner of the recommender once deployed.




How to Cite

Szlávik, Z., Kowalczyk, W., & Schut, M. (2021). Diversity Measurement of Recommender Systems under Different User Choice Models. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 369-376. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14116