Abstract: Most of existing recommendation algorithms depend on items' rating records marked by users and pay less attention to users' individual requirements such that the recommendation results may not really conform to the practical demands. Aiming at the above shortcoming, by integrating genre and density-peaks-clustering technique, this paper proposes an improved user-based collaborative filtering recommendation algorithm for meeting various individual needs of users. Firstly, the users' interests in different genres are analyzed according to historical rating data and the genre of items. And then, the density peaks clustering algorithm is utilized to research items' attributes and analyze users' interests in different clusters of items, by which the users' individual demand can be dug deeply. Since the proposed method uses density peaks clustering, it doesn't need to assign the centers of clustering uses and specify the quantity of clusters before clustering, which can increase the accuracy of clustering. Moreover, this paper uses inverse document frequency to optimize the proposed algorithm such that the identification degree of items' characteristics and users' interests can be effectively improved. Finally, it is shown from the experiment with the movielens dataset that the proposed algorithm can get users' interests effectively and provide more accurate and efficient Top-N recommendation.