Research on the HHRA algorithm of the new college entrance examination volunteer filling recommendation system
In recent years,the number of people taking the college entrance examination in China has been increasing year by year,and competition has become increasingly fierce.The importance of filling out college entrance examination application is self-evident.A HHRA hybrid recommendation algorithm is proposed based on a survey of college entrance examination volunteer data to address the current problems in filling out college entrance examination applications.The algorithm first constructs a user feature matrix and uses the min max method for standardization;Secondly,an improved Pearson correlation coefficient is used for similarity calculation and a recommendation set is generated.Then calculate the admission probabilities of different volunteers,and divide the levels of college volunteers according to the admission probabilities.Finally,extract candidate preference features from the original volunteer table,calculate preference similarity,and obtain the final volunteer recommendation result.Research has shown that using the HHRA algorithm can better utilize scores and meet users'personalized volunteer needs.
college entrance examination volunteerHHRA algorithmrecommendation algorithmJaccard coefficient
NETL
NSTL
万方数据
