Research On Collaborative Filtering Algorithm Based On Model Users

In an era of e-commerce which is becoming popular and popular, our need is no longer a simple provision of information but targeted information recommendation. Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms. But with the development and growth of e-commerce industry, whether the number of users or goods increased exponentially, and the need of users for e-commerce recommendation services are increasingly higher. Collaborative filtering technology reveals a number of bottlenecks to be addressed in the face of current challenges. To address these problems, domestic and foreign research institutions and scholars continue to explore the improvement program. This paper does in-depth analysis and comparison of the collaborative filtering algorithm and improves the current principal algorithm, proposes an collaborative filtering algorithm based on the model users.The concept of the user model is similar to working model or a model in real life. Model users play an exemplary role in a particular field or industry which others follow and learn from. The main purpose of introducing such a concept to the collaborative filtering algorithm is to build a better stability of the model user pattern, by which the model users can reflect the user's interests the one or more fields. the products recommended by collaborative recommendation should be accurate and reliable. This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time. At the same time a stable model user pattern can also respond to challenges of the rapidly growing users and commodities.This paper generates a model user score vector in each class to represent the user's overall evaluation of such trends by the user and project evaluation matrix cluster. Model users is not the center of the users but virtual users generated according to certain rules. This group of model users increases the user's score density and reflects the overall evaluation of trends in certain class users.Clustering techniques usually have to assign the number of clusters, but whether the result really reflects the classification of users needs verification on the validity of cluster. This paper introduces two cluster validity indexes: DB indicators and split factor PC. Verifying HCM and FCM clustering strategy by the two cluster validity indices can achieve the optimal cluster size when validity indexes reach the extreme values. Both of HCM and FCM realize adaptive determination of cluster size.Experimental results show that adaptive clustering algorithm can obtain the local optimal validation index values, that is, the best clustering effect. Collaborative filtering algorithm based on model users greatly improves the efficiency of online recommendation, makes model users relatively stable and also improves the accuracy of recommendation.