A New Statistical Density Clustering Algorithm Based on Mutual Vote and Subjective Logic Applied to Recommender Systems
Title | A New Statistical Density Clustering Algorithm Based on Mutual Vote and Subjective Logic Applied to Recommender Systems |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Haydar, Charif, Boyer, Anne |
Conference Name | Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4635-1 |
Keywords | clustering, Human Behavior, human factors, pubcrawl, recommender systems, resilience, Resiliency, Scalability, subjective logic |
Abstract | Data clustering is an important topic in data science in general, but also in user modeling and recommendation systems. Some clustering algorithms like K-means require the adjustment of many parameters, and force the clustering without considering the clusterability of the dataset. Others, like DBSCAN, are adjusted to a fixed density threshold, so can't detect clusters with different densities. In this paper we propose a new clustering algorithm based on the mutual vote, which adjusts itself automatically to the dataset, demands a minimum of parameterizing, and is able to detect clusters with different densities in the same dataset. We test our algorithm and compare it to other clustering algorithms for clustering users, and predict their purchases in the context of recommendation systems. |
URL | https://dl.acm.org/citation.cfm?doid=3079628.3079676 |
DOI | 10.1145/3079628.3079676 |
Citation Key | haydar_new_2017 |