A Multidimensional Model for Recommendation Systems Based on Classification and Entropy

The proliferation of false and redundant information on e-commerce platforms as well as the prevalence of ineffective recommendations and other untrustworthy behaviors has seriously impeded the healthy development of these platforms. To address these issues and enhance prediction accuracy and user trust, contemporary recommendation systems often utilize additional information (i.e., side information). In this work, we propose a model to improve the recommendation quality by employing the information entropy of user-item ratings. The entropy was used as side information to reflect the global rating behavior of the user and item. We also utilized the classification of the user and item as heuristic information to improve the prediction quality. In our best result, we achieved a significant improvement of 8.2% in prediction accuracy. The model classified the items by the users’ actual preference, which is more trustworthy for users. We evaluated our model with three real-world datasets. The performance of our proposed model was significantly better than the other baseline methods. The similarity calculation method employed in the present model has the potential to mitigate the data sparsity problem associated with correlation-based similarity. The proposed weight matrix has zero sparsity. Furthermore, the proposed model has a more favorable computational complexity for prediction compared to the conventional k-nearest neighbor method.