A comparative study of algorithms used in recommender systems : measuring their accuracy on cold start data

Sammanfattning: A common problem today is the increasing amount of information that people get exposed to, an issue that recommender systems aim to fix through giving personalized recommendations. The most widely used technique within recommender systems is collaborative filtering, which uses the similarity between users who share the same interest. A recommendation can, therefore, be done by finding users with similar interests and finding new items through those. A known problem within recommender systems is the cold-start problem. This arises when a new user or a new item is added to the system. Due to limited information about those, it becomes more difficult to generate accurate personalized recommendations. The aim of this report is to study how a set of algorithms within collaborative filtering performs during the cold-start problem. The chosen algorithms are SVD, SVD++, and Slope One. Both SVD and SVD++ belong to a model- based approach, and Slope One belongs to a memory-based approach, two categories that algorithms within collaborative filtering are divided into. The result of the study indicates that the memory-based algorithm, Slope One, is less accurate and has lower performance than the model-based algorithms, SVD and SVD++, which is in line with previous research. Regarding SVD and SVD++, further studies need to be conducted in order to conclude which of them performs the best during the cold-start problem.