Research on Fine Students'Dynamic Information Recommendation in Smart Campus Based on Collaborative Filtering and Indoor 3D Positioning
As a new educational environment,the smart campus often has the problem of information over-load because of its huge amount of internal resources.However,the current undifferentiated information recommendation technology is easy to ignore the individual differences of students,and it is difficult to meet the specific needs of students in different grades and majors.In order to solve such problems,the research first uses the indoor three-dimensional positioning algorithm based on the improved K-means clustering algorithm to locate the students'positions,and then uses the collaborative filtering algorithm based on the weight matrix to recommend the students'demands for refined and dynamic information.The research results show that the positioning accuracy and error of the indoor 3D positioning algorithm are 91.13％and 0.930 1 m,respectively,which is obviously better than the indoor three-dimensional position-ing algorithm before improvement.And when the adjustment parameter of correlation similarity and Tanimoto coefficient is 0.4,the MAE value of collaborative filtering algorithm based on weight matrix is generally lower than 0.51.To sum up,the indoor three-dimensional positioning algorithm and collabora-tive filtering algorithm proposed in this study have superior performance,and can make appropriate recommendations for different students'needs in the background of smart campus.
万方数据
维普
