A Neural Network Recommendation Approach for Improving the Accuracy of Multi-criteria Collaborative Filtering

摘要

Recommender systems (RSs) are intelligent decision-making tools that exploit users' preferences and suggest items that might be interesting to them. Traditionally, RSs use single ratings to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various items' attributes for improving the prediction accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed in employing artificial neural networks (ANNs) to determine the predictive performance of the systems based on aggregation function approach. The empirical results of the proposed techniques are compared with that of the traditional single rating-based techniques.