Recommendation method based on heterogeneous context perception

Abstract

The invention discloses a recommendation method based on heterogeneous context perception. The method is characterized by comprising the steps that (1) a user-item score matrix, interactive context information and property context information are acquired; (2) an interactive context score prediction function and an interactive context perception model are constructed based on a tensor decomposition technology; (3) a property context score prediction function and a property context perception model are constructed based on a matrix decomposition technology; (4) semi-supervised cooperative training is performed on the interactive context perception model and the property context perception model; and (5) weight fusion scoring is performed on the interactive context perception model and the property context perception model, and then recommendation is performed according to scores. Through the method, influences of property context information and interactive context information on recommendation can be perceived, the problem of data sparsity of a recommendation system can be relieved, and the accuracy of recommendation is improved.

Description

technical field [0001] The invention relates to the field of recommendation systems, in particular to a recommendation method based on heterogeneous context perception. Background technique [0002] In the field of traditional collaborative filtering recommender systems, only focus on the similarity relationship between users or items or the "user-item" interaction relationship, however these are often vulnerable to the data sparsity problem. In fact, contextual information also affects the recommendation system, such as someone prefers to read in the morning, and employees prefer to have lunch near the company. Therefore, integrating these contextual information into the recommendation system can alleviate the data sparsity problem of the recommendation system and improve the accuracy of the recommendation. [0003] The context information can often be divided into two categories, the first category is user-item attribute context information, and the second category is use...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the data sparsity problem of the current recommendation algorithm, the present invention proposes a recommendation method based on heterogeneous context perception, that is, incorporates the impact of two different types of context on recommendation, and uses a semi-supervised collaborative training algorithm to further alleviate data Sparsity problem, while using the semi-supervised collaborative training algorithm to optimize the two constructed context-aware models, and then merge them into a final recommendation model; finally use the root mean square error index to measure the performance of the recommendation algorithm

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