Collaborative filtering recommendation algorithm towards intelligent community
Wei Qing Fu, Jun Liu, Yirong Lai · Discrete and Continuous Dynamical Systems - S · 2018
Collaborative filtering recommendation algorithm is a successful and widely used recommendation method in recommender system. In the collaborative filtering recommendation algorithm, the key step is to find the nearest neighbor. Combined with the application scenario of the intelligent community, Pearson Correlation Coefficient is introduced to improve the accuracy of similarity calculation. At the same time, considering that the residents are relatively fixed, the K-means clustering algorithm can be combined with the user-based collaborative filtering recommendation algorithm to improve the sparsity of the matrix and improve the speed of recommendation. Validation results on MovieLens dataset show that the collaborative filtering recommendation algorithm integrating with K-means clustering algorithm and community factors can more effectively predict the actual user rating in the community application scenario, and improve the recommendation accuracy and recommendation speed, compared with the traditional collaborative filtering recommendation algorithm.