FR method for optimizing personalized recommendation results

Abstract

The invention discloses a failure record (FR) method for optimizing personalized recommendation results, which improves the personalized recommendation quality and precision by using social tag network filter and recommendation deviation removal. The social tag network filter method comprises the following steps of: establishing a project social network K neighbor by using a social tag network model, and constructing a social tag filter set during recommending in a recommendation model based on the project social network K neighbor, wherein the social tag filter set is used for filtering recommended projects with low social tag relevance in the user scored projects in the recommendation results of a project-orientated K neighbor model so as to combine information in user-project scoring data and social tag network data to recommend. The recommendation deviation removal comprises the following steps of: based on prediction values of the project-orientated K neighbor model on the known user-project scoring data and a turn score of the user, estimating the recommendation deviation by using a linear model; and when the recommendation is performed by using the recommendation model, removing the corresponding recommendation deviation estimation values from the scoring prediction values so as to optimize the recommendation results.

Description

technical field [0001] The invention relates to an FR method for optimizing personalized recommendation results, which is suitable for personalized recommendation in e-commerce and belongs to the technical fields of information retrieval and data mining. Background technique [0002] A mature e-commerce system often has a huge number of users; at the same time, frequent user registration and logout also makes user data very unstable. Therefore, in practical applications, it is difficult for the user-oriented K-nearest neighbor model to provide efficient and stable recommendation services. In order to solve this drawback, based on the fact that the number of items in the e-commerce system is often far less than the number of users, and the item data is more stable, Sarwar et al. proposed an item-oriented K-nearest neighbor model. The item-oriented K-nearest neighbor model uses the relationship modeling between items instead of modeling the relationship between users. It is a...

AI Technical Summary

Problems solved by technology

However, the above methods have gone to the other extreme, that is, only using personalized label data for recommendation, and the recommendation results are also based on a single data source.

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