摘要：Memory-based collaborative filtering (CF) is applied to help users to find their favorite items in recommender systems. Up to now, this approach has been proven successful in recommender systems, such as e-commerce systems. The idea of this approach is that the interest of a particular user will be more consistent with those who share similar preference with him or her. Therefore, it is critical that an appropriate similarity measure should be selected for making recommendations. This paper proposes a new similarity measure named adjusted Euclidean distance (AED) method which unifies all Euclidean distances between vectors in different dimensional vector spaces. Our AED enjoy the advantages that it takes both the length of vectors and different dimension-numbers of vector spaces into consideration. Based on two datasets MovieLens and Book-Crossing, we conduct experiments comparing our AED with two notable existing methods. The experimental results demonstrate that our AED improves the accuracy of prediction and recommendation.
