Measuring similarity between user profile and library book

Abstract

In the development of recommender system either the content or collaborative filtering is necessary. To filter the records it is required to measure the similarity between profile of user and items present in the dataset. This experiment is performed on the dataset containing 978 books related to computer science field and 7 users. Similarity between profile of user and contents of book is measured using Euclidean, Manhattan, Minkowski, Cosine distances. The results are evaluated and compared. This work is useful in the development of library recommender system.