Abstract: Collaborative filtering algorithms are widely used in recommendation systems, with a key research focus on how to achieve effective user clustering and identify more accurate sets of similar neighbors. One of the key research focuses is to achieve user clustering and identify more similar neighbor sets. To enhance the accuracy of classification and prediction in these algorithms, this paper proposes a movie recommendation algorithm based on user profile similarity. First, based on a tag set of movie content features, a user preference profile matrix is constructed by calculating the frequency of user ratings among different movie content tags. Then, user similarity is calculated through this matrix, and a user complex network is modeled to determine the centrality weight of users within the network. Finally, the community weight is obtained through K-core decomposition of the user network. The rating predictions are improved by integrating the centrality weight and community weight of neighboring users. Experimental results show that the proposed algorithm improves prediction accuracy and classification accuracy by 2.72% and 3.17%, respectively. These results validate the effectiveness of complex network modeling based on user profile similarity for enhancing information utilization in recommendation systems.

Key words: user profile, collaborative filtering, similarity, complex network, classification accuracy