Biclustering ARTMAP Collaborative Filtering Recommender System
Collaborative filtering provides recommendations based on the behavior of each user combined with behavior of users with similar interests. Recommender systems are becoming widespread, helping people choose movies, books, and things to buy. In this study, we examine the use of Biclustering ARTMAP to build a collaborative filtering recommendation system. We introduce a novel modification to how the Biclustering ARTMAP algorithm computes the item-cluster similarity, and a way to adapt it for the prediction of user ratings. We apply the algorithm to the MovieLens 100k dataset, and find that it achieves promising performance compared to other collaborative filtering techniques.
I. O. Elnabarawy et al., "Biclustering ARTMAP Collaborative Filtering Recommender System," Proceedings of the International Joint Conference on Neural Networks (2016, Vancouver, Canada), Institute of Electrical and Electronics Engineers (IEEE), Jul 2016.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2016.7727578
2016 International Joint Conference on Neural Networks, IJCNN 2016 (2016: Jul. 24-29, Vancouver, Canada)
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Behavioral Research; Recommender Systems; Bi-Clustering; Collaborative Filtering Recommendations; Collaborative Filtering Recommender Systems; Collaborative Filtering Techniques; Item Clusters; Movielens; Similar Interests; User Rating; Collaborative Filtering
Article - Conference proceedings
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