Hospitalizing resource scoring and recommending method based on latent factor model

Abstract

The invention provides a hospitalizing resource scoring and recommending method based on a latent factor model. The method comprises the steps that 1, recommended resource item data are acquired, and data filtering and cleaning are conducted; 2, each parameter in a learning automaton training SVD++ model with continuous actions is used; 3, for a user and all resource items in a set, a scoring predicted value of the user to each resource item is calculated based on the latent factor model; 4, a TopN recommendation list is obtained by using an improved sorting algorithm. According to the hospitalizing resource scoring and recommending method based on the latent factor model, a prediction scoring result and correlation degree of a most relevant implicit type are both taken as basis for generating the recommendation list; meanwhile, benchmark coefficients of the resource items are used for reference, it can be guaranteed that the recommended resource items are more likely to attract interest of a patient, and meanwhile it can be guaranteed that the resource items have high enough scores to obtain the love of the patient.

Description

technical field [0001] The present invention relates to the hidden semantic model score prediction and TopN recommendation application of medical resources in the field of medical big data, specifically, it relates to the medical treatment based on latent semantic model in medical big data based on the combination of learning automata and gradient descent algorithm Resource scoring and recommendation methods. Background technique [0002] Scoring and recommendation functions have been widely used in today's Internet products, such as Taobao's product recommendation, Facebook's friend recommendation, Baidu's news recommendation, etc. Product recommendations, personalized product recommendations, friend recommendations from various social networking sites, popular application recommendations, etc. Recommendations are truly ubiquitous and pervasive, and the proportion of value generated by recommendations has always been high. For the above recommendation problems, the recomme...

AI Technical Summary

Problems solved by technology

Overall, however, recommendation algorithms are still a subject for further research

[0003] After searching, no description or report of similar technology to the present invention has been found at present, and similar materials at home and abroad have not been collected yet.

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