Research Article
Balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems
@INPROCEEDINGS{10.1007/978-3-031-28816-6_9, author={Hiep Xuan Huynh and Nhung Cam Thi Mai and Hai Thanh Nguyen}, title={Balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems}, proceedings={Context-Aware Systems and Applications. 11th EAI International Conference, ICCASA 2022, Vinh Long, Vietnam, October 27-28, 2022, Proceedings}, proceedings_a={ICCASA}, year={2023}, month={3}, keywords={Balltree Similarity measure Movielens Recommender systems Spatial partitioning}, doi={10.1007/978-3-031-28816-6_9} }- Hiep Xuan Huynh
Nhung Cam Thi Mai
Hai Thanh Nguyen
Year: 2023
Balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems
ICCASA
Springer
DOI: 10.1007/978-3-031-28816-6_9
Abstract
The recommender systems have been widely applied in numerous applications that support online retailers, video sharing websites, medical systems, etc. Similar measures are essential in providing valuable recommendations to users in such systems. This work presents a novel approach, namelyBall-Sim, with a new similarity metric using a balltree structure for recommender systems. Furthermore, we want to leverage the tree structure to determine the closest k nearby users to improve the recommender systems’ efficiency. The work’s experimental scenarios outlined the steps of building a balltree and identifying nearby users based on the tree structure. Besides, the work also evaluates the implemented recommender system by comparing the recommender system’s results based on the balltree-based spatial partitioning with the recommender system using the default parameters. The data used in the experiments is the Movielens dataset, a web-based film recommender system, and an important data source for evaluating the studies, with 100,000 samples, including ratings from 943 users for 1,664 movies. The results show that the recommender system with a balltree-based similarity metric can improve the accuracy compared to a commonly-used measure such as the cosine metric.
