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A novel Adaptive Genetic Neural Network (AGNN) model for recommender systems using modified k-means clustering approach

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Abstract

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.

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  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    C. Selvi & E. Sivasankar

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  1. C. Selvi
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Selvi, C., Sivasankar, E. A novel Adaptive Genetic Neural Network (AGNN) model for recommender systems using modified k-means clustering approach. Multimed Tools Appl 78, 14303–14330 (2019). https://doi.org/10.1007/s11042-018-6790-y

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