Accuracy Assessment of Similarity Measures in Collaborative Recommendations Using CF4J Framework
Vijay Kumar Verma, Rajesh Kumar Aggarwal · International Journal of Modern Education and Computer Science · 2019
There are various libraries that facilitate the design and development of recommender systems (RSs) research in both the academia and industry.Different libraries provide a different set of functionalities based on their foundational design principles.When new algorithms are proposed, researchers need to compare these against prior algorithms considering many challenges such as reproducibility of results, evaluation metrics, test harnesses, etc.Although many open source RS libraries exist to carry out research experiments and provide a varying degree of features such as extensibility, performance, scalability, flexibility, etc.To that end, this paper describes a recently introduced open-source RS library, Collaborative Filtering for Java (CF4J), which is specially designed for collaborative recommendations.Firstly, the brief internals of the CF4J framework are explained and it has been compared with other related libraries such as LibRec, LensKit, and Apache Mahout based on the recommendation approaches and evaluation tools.Secondly, we have summarized all the state-of-art similarity measures provided by the CF4J library.Finally, in order to determine the accuracy of these similarity measures, several experiments have been conducted using standardized benchmark datasets such as MovieLens-1M, MovieLens-10M, and MovieLens-20M.Empirically obtained results demonstrate that the Jaccard-Mean Squared Difference (JMSD) similarity measure provides better recommendation accuracy among all similarity measures.