Collaborative Filtering Recommendation Algorithm Optimization Based on User Attributes

Aiming at the data sparse and cold start problems in collaborative filtering recommendation algorithm, an optimized solution based on user characteristics and user ratings is proposed in this paper. Based on users´ basic attributes and users´ history score record, the similarity of users and the similarity of items are calculated, and the nearest neighbor users and similar items are obtained. The advantage of the algorithm is that it combines the user´s score and personal attributes to calculate the similarity between users and to recommend items. The optimized algorithm is applied to the recommendation of insurance products. Experiments based on real data from insurance company show that this method can reduce the average absolute error and improve the accuracy of recommendation.