Rule-Based Effective Collaborative Recommendation Using Unfavorable Preference

The biggest challenge in collaborative filtering recommendation system research is data sparsity; it mainly occurs as user rates very few items from widely available options. Data Imputation techniques address the data sparsity problem by filling the missing values and then predicting the likeliness of the user. Most of the existing imputation systems assign high ratings to the items or incorporate additional information to enhance the performance of collaborative filtering recommendations. This paper proposes an association rule-based imputation method (RUBLE) to improve the top-N prediction performance of the collaborative filtering recommendation. The proposed method identifies the unfavorable items of each user using the association rule mining technique and imputes them with low values. The proposed method not only addresses the sparsity problem but also provides a better quality of recommendation by eliminating the unfavorable items in top-N predictions. Existing collaborative methods can quickly adapt to the proposed method as it is method agnostic. The experimental results show that the proposed method enhances the accuracy of the traditional recommender methods by two times on average and significantly outperforms existing imputation based approaches.