Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique

Objectives: As the plenty of Web services on the Internet increases, developing efficient techniques for Web service recommendation has become more significant. The main objective of this paper is to compare and study the drawbacks of the performance of different existing similarity measures against the proposed similarity measure that use the concept of collaborative filtering technique. Methods/Analysis: Collaborative filtering has turned into one of the most used technique to give personalized services for users. The key of this technique is to find alike users or items using user-item rating matrix such that the system can show recommendations for users. Experiments on Web Service (WSDL) data sets are conducted and compared with many traditional similarity measures namely Pearson correlation coefficient, JacUOD, Bhattacharyya coefficient. The result shows the superiority of the proposed similarity model in recommendation performance. Findings: However, existing approaches related to these techniques are derived from similarity algorithms, such as Pearson correlation coefficient, mean squared distance, and cosine. These methods are not much efficient, particularly in the cold user conditions. Applications/Improvement: This paper presents a new user based similarity calculation model to enhance the recommendation performance and to estimate the similarities for each user. The proposed model incorporates two traditional similarity measures namely Pearson Correlation Coefficient and Jaccard Coefficient.