Abstract: The ever-increasing number of users and it ems of modern electronic commercial system has made the user-item matrix to become more and more sparse．This situation,in combination with somewhat inappropriate similarity calculation methods currently used,maks the recommendation quality of recommender system to gradually reduce．For this,we presented an optimized recommender algorithm which is based on a hybrid model.In our algorithm,the similarity function is a linear combination of the item property similarity and a modified correlation cosine similarity．The weighting factor,which is generated automatically,is related to the number of users who rated both items．The modification to the correlation cosine similarity measure considers both the rating tendency and the activity from users．To deal with the cold start problem,we also acquired user similarity through user property information with weighting factors computed by SVDFeature．The experimental results demonstrate that our algorithm effectively improves the recommendation quality and alleviates cold starting problem resulting from both users and items.

Key words: Collaborative filtering,Similarity,Hybrid recommendation,Weighting factor,Cold start