Similarity Algorithm Based on User's Common Neighbors and Grade Information

With the rapid growth of Internet,recommender systems have been used in many fields,and collaborative filtering(CF) is one of the earliest and the most successful ones.CF method usually identifies the neighborhood of each user based on similarity between two users; then predicts items' rating by integrating ratings of target user's neighbors,and lastly those items with higher predicted score are recommended to target user.So similarity plays an important role and affects the accuracy of the prediction.Up to now,various similarity measures have been proposed by resear-chers from different aspect.And common-neighbor algorithm is a simple and efficient method.However,common-neighbor algorithm just considers the number of common objects scored by two users,doesn't consider the user's grade information.In this paper,an improved algorithm based on common-neighbor and user's grade information was proposed.Experimental results indicate that improved common-neighbor algorithm can obtain rather good predicting results.