An Adjusted Recommendation List Size Approach for Users' Multiple Item Preferences


Peker S., KOÇYİĞİT A.

17th International Conference on Artificial Intelligence - Methodology, Systems and Applications (AIMSA), Varna, Bulgaria, 7 - 10 September 2016, vol.9883, pp.310-319 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 9883
  • Doi Number: 10.1007/978-3-319-44748-3_30
  • City: Varna
  • Country: Bulgaria
  • Page Numbers: pp.310-319

Abstract

This paper describes the design and implementation of a novel approach to dynamically adjust the recommendation list size for multiple preferences of a user. By considering users' earlier preferences, machine learning techniques are employed to estimate the optimal recommendation list size according to current conditions of users. The proposed approach has been evaluated on real-life data from grocery shopping domain by conducting a series of experiments. The results show that the proposed approach achieves better overall recommendation quality than the standard approach and it outperforms the benchmark method in efficiency by shortening the recommendation list while maintaining the effectiveness.