Improved GMMKNN Hybrid Recommendation Algorithm

• Published in: Journal of physics. Conference series Vol. 2747; no. 1; pp. 12032 - 12044
• Main Authors: Hao, Yaxian, Feng, Lijiao
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2024
• Subjects: Clustering; Gaussian kernel; GMM; K-nearest neighbors algorithm; Kernel functions; KNN algorithm; Probabilistic models; Recommendation system
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/2747/1/012032

This paper proposes a hybrid recommendation algorithm that combines the advantages of Gaussian Mixture Model (GMM) and K-Nearest Neighbors (KNN) algorithms. The algorithm first applies GMM to cluster the training data, grouping users with similar interests using clustering techniques. It then utilizes the KNN algorithm for prediction. During the KNN recommendation process for a target user, the algorithm searches for neighboring users with similar interests and features within the same cluster. This significantly reduces the search scope of the nearest neighbors, thanks to the assistance of the GMM algorithm. The algorithm then selects the K most similar neighbors from the target user’s cluster as the candidate pool for personalized recommendations. Additionally, the algorithm improves the weight calculation method by incorporating a Gaussian kernel function for weight estimation. Experimental results on the dataset demonstrate that the proposed improved algorithm effectively enhances the accuracy of prediction results.