The Recommender System (RS) plays an important role in information retrieval techniques in a bid to handle massive online data effectively. It gives suggestions on items/services to the target online user to ensure correct decisions quickly and easily. Collaborative Filtering (CF) is a key approach in RS providing a recommendation to the target online user, based on a rating similarity among users. Unsupervised clustering approach is a model-based CF, which is preferred as it ensures simple and effective recommendation. Such CFs suffer from a high error rate and needs additional iterations for convergence. This paper proposes a Modified k-means clustering approach to eliminate the above mentioned issues to provide well-framed clusters. The novel supervised Adaptive Genetic Neural Network (AGNN) method is proposed to locate the most favored data points in a cluster to deliver effective recommendations. The performance of the proposed RS is measured by conducting an experimental analysis on benchmark MovieLens and Netflix datasets. Results are compared with state-of-the-art methods namely Artificial Neural Network (ANN) and Fuzzy based RS models to show the effectiveness of the proposed AGNN method.
C. Selvi and Elango, S., “A novel Adaptive Genetic Neural Network (AGNN) model for recommender systems using modified k-means clustering approach”, Multimedia Tools and Applications, pp. 1–28, 2018.