Novel centroid selection approaches for KMeans-clustering based recommender systems

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer
a given item when making a choice. Over the years, this process has been dependent on robust applications of data
mining and machine learning techniques, which are known to have scalability issues when being applied for recommender
systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability
issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that
they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline
training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems
can improve performance as well as being cost saving. The proposed centroid selection method has the ability to
exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in
comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach
has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music
domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker
than existing approaches, which in turn improves accuracy of the recommendation provided.