| Title | An Efficient Recommender System Based on Collaborative Filtering Recommendation and Cluster Ensemble |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zarzour, Hafed, Maazouzi, Faiz, Al–Zinati, Mohammad, Jararweh, Yaser, Baker, Thar |
| Conference Name | 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) |
| Keywords | cluster ensemble, clustering methods, collaborative filtering, collaborative filtering recommendation, EM, expectation maximization, Filtering, Human Behavior, Partitioning algorithms, Prediction algorithms, pubcrawl, recommender system, recommender system for big data, recommender systems, resilience, Resiliency, Scalability, social networking (online), System performance |
| Abstract | In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy. |
| DOI | 10.1109/SNAMS53716.2021.9732118 |
| Citation Key | zarzour_efficient_2021 |
An Efficient Recommender System Based on Collaborative Filtering Recommendation and Cluster Ensemble