A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment

AbstractMemory based algorithms, often referred to as similarity based Collaborative Filtering (CF) is one of the most popular and successful approaches to provide service recommendations. It provides automated and personalized suggestions to consumers to select variety of products. Typically, the core of similarity based CF which greatly affect the performance of recommendation system is to finding similar users to a target user. Conventional similarity measures like Cosine, Pearson correlation coefficient, Jaccard similarity suffer from accuracy problem under sparse environment. Hence in this paper, we propose a new similarity approach based on Mean Measure of Divergence that takes rating habits of a user into account. The quality of recommendation of proposed approach is analyzed on benchmark datasets: ML 100K, ML-1M and Each Movie for various sparsity levels. The results depict that the proposed similarity measure outperforms existing measures in terms of prediction accuracy

Performance of known ratings-based multi-criteria recommender system for housing selection

Housing developments are increasingly massive, and the lack of available information makes prospective customers experience difficulties in choosing a housing. These conditions resulted in the need for a recommendation system to assist consumers in choosing a place to live. In this study, we propose using the Multi-Criteria Recommender System (MCRS) to produce the most recommended housing selection recommendations in a case study of five housing complexes in Malang Raya. The system generates recommendations based on known user rating of 14 criteria and an overall rating (R0) stored in the database. In the experimental stage, the MCRS system in this study used four different methods: cosine, adjust cosine, Pearson correlation, and spearman rank-order correlation coefficient. The test results show that the recommendation system with each similarity method can produce housing recommendations by displaying the three most relevant housing recommendations to the user. Next, we use a confusion matrix to analyze the accuracy of the recommendations generated by the four similarity methods. The results of the confusion matrix calculation show that the average accuracy value for cosine-based similarity is 63.8%, the adjusted-cosine similarity is 70.4%, the Pearson correlation is 88.7%, and the Spearman rank-order correlation coefficient is 75.57%