Recommender System Based on Expert and Item Category

Authors

  • Thanaphon Phukseng King Mongkut’s University of Technology North Bangkok
  • Sunantha Sodsee King Mongkut’s University of Technology North Bangkok

DOI:

https://doi.org/10.4186/ej.2018.22.2.157

Abstract

The objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique, 2) the experts from the frequency of rating technique, and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creating.

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Author Biographies

Thanaphon Phukseng

Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok,
Bangkok 10800, Thailand

Sunantha Sodsee

Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok,
Bangkok 10800, Thailand

Published

Vol 22 No 2, Mar 30, 2018

How to Cite

[1]
T. Phukseng and S. Sodsee, “Recommender System Based on Expert and Item Category”, Eng. J., vol. 22, no. 2, pp. 157-168, Mar. 2018.