Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two specific recommender algorithms, namely FunkSVD, a matrix factorization algorithm and Item-based collaborative filtering, which utilizes item similarity. This study aims to compare the prediction accuracy of the algorithms when ran on a small and a large dataset. By performing cross-validation on the algorithms, this paper seeks to obtain data that supposedly may clarify ambiguities regarding the accuracy of the algorithms. The tests yielded results which indicated that the FunkSVD algorithm may be more accurate than the Item-based collaborative filtering algorithm, but further research is required to come to a concrete conclusion.
Place, publisher, year, edition, pages
IdentifiersURN: urn:nbn:se:kth:diva-186456OAI: oai:DiVA.org:kth-186456DiVA: diva2:927356