Data analysis for collaborative filtering systems
Toon De Pessemier · Ghent University Academic Bibliography (Ghent University) · 2010
The overabundance of information and the related difficulty to discover interesting content has complicated the selection process for the end-user who gets overloaded with data and risks to get lost. Recommender systems try to assist users in this content-selection process by using intelligent personalisation techniques which filter the information. Most commonlyused recommendation algorithms are based on Collaborative Filtering (CF) techniques which generally provide better results than ContentBased (CB) techniques and require no metadata of the content [1]. This research investigates the amount of data required for these CF algorithms.