The Effect of Similarity Support in K-Nearest-Neighborhood Based Collaborative Filtering
Ouyang Yuan · Chinese Journal of Computers · 2010
Recommender systems which can provide people with personalized suggestions usually rely on Collaborative Filtering(CF).A classical approach to CF is based on k-nearest-neighborhood(kNN) model,where the most important task is constructing the kNN sets for involved users or items.However,when constructing kNN sets,there is a dilemma to decide the value of k —A too small value will lead to poor recommendation performance,whereas a too large one will result in unacceptable computational complexity.In this work the authors first empirically validated that the suitable value of k in kNN based CF was affected by the number of the totally involved entities,and then focused on improving the quality of the kNN sets in kNN based CF for providing high recommendation performance as well as maintaining suitable kNN set size.To achieve this objective,the authors propose a novel kNN metric named Similarity Support(SS).By taking SS into consideration during the kNN building process,the authors design a series of strategies for optimizing kNN based CF.The empirical studies on public large,real datasets show that due to the improvement on the quality of kNN set brought by SS,CF adjusted by the new strategies turned out to be superior to kNN based CF in term of both recommendation performance and computational complexity.