Comparison of User Based and Item Based Collaborative Filtering Recommendation Services
Peter J. Boström, Melker Filipsson · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017
With a constantly increasing amount of content on the internet, filtering algorithms are now more relevant than ever. There are several different methods of providing this type of filtering, and some of the more commonly used are user based and item based collaborative filtering. As both of these have different pros and cons, this report seeks to evaluate in which situations these outperform one another, and by how big of a margin. The purpose of this is getting insight in how some of these basic filtering algorithms work, and how they differ from one another. An algorithm using Adjusted Cosine Similarity to calculate the similarities between users and items, and RMSE to compute the error, was executed on two different datasets with differing sizes of training and testing data. The datasets had the same amount of ratings but the second had less spread in the number of items in the set. The results were similar although slightly superior for both user and item based filtering on the second dataset compared to the first one. Conclusively, when dealing with datasets that are large enough for practical use, user based collaborative filtering proves to be superior in all reviewed cases.