Recommendation using Rule based Implicative Rating Measure

Abstract: The paper presents a rule based implicative rating measure to calculate the ratings of users on items. The paper also presents a new model using the ruleset with the rule length of 2 and the proposed measure to suggest to users the list of items with the highest ratings. The new model is compared to the three existing models that use items (such as the popular items, the items with highest similarities, and the items with strong relationships) to make the suggestion. The experiments on the MSWeb dataset and the MovieLens dataset indicate that the proposed recommendation model has the higher performace (via the Precision - Recall and the ROC curves) than the compared models for most of the given.

Lan Phuong Phan, Hung Huu Huynh and Hiep Xuan Huynh. “Recommendation using Rule based Implicative Rating Measure”. International Journal of Advanced Computer Science and Applications (IJACSA) 9.4 (2018). http://dx.doi.org/10.14569/IJACSA.2018.090428

@article{Phan2018,
title = {Recommendation using Rule based Implicative Rating Measure},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090428},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090428},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {4},
author = {Lan Phuong Phan and Hung Huu Huynh and Hiep Xuan Huynh}
}