Research on schedule-based user recommendation model based on improved K-means algorithm

Nowadays all kinds of social platforms provide various recommendation services, greatly enriching people's life. In the condition that social platforms have become indispensable tools, people have more to except that the social platforms can provide more convenient and efficient services for life and work. With three dimensions of context: time, place and activity, this paper presents one new real-time recommendation methodology based on user's schedule. It transforms the schedule-based user recommendations into text clustering using K-means algorithm. Due to the predefined limit of value K in traditional K-means algorithm, the K-means algorithm is improved and an application of improved K-means algorithm in the recommendations model of schedule-based users is introduced in this paper. The results of relevant experiments and analysis indicate that the system improved the recall and precision than traditional one and has practical application value.