Collaborative filtering method on basis of scene implicit relation among articles

Abstract

The invention discloses a collaborative filtering method on the basis of a scene implicit relation among articles. The collaborative filtering method comprises the following steps of: 1, extracting scores of the articles in different scenes from original score data and establishing an article-scene score matrix; 2, decomposing the article-scene score matrix by a matrix decomposition method to obtain an implicit factor matrix of the articles; 3, establishing a scene feature vector for each article by using the obtained implicit factor matrix of the articles so as to calculate the similarity among the articles by utilizing a Pearson correlation coefficient and establish an article implicit relation matrix; and 4, integrating obtained article implicit relation information into a probability matrix decomposition matrix to generate a personalized recommendation. According to the invention, scene information can be sufficiently utilized to mine the implicit relation information among the articles, and the recommendation is generated by utilizing the implicit relation among the articles; the collaborative filtering method has high expandability for the scene information, and a candidate scene set can be regulated according to the application requirements; and the accuracy and the personalization degree of the recommendation can be effectively improved.

Description

technical field [0001] The invention relates to a collaborative filtering method, in particular to a collaborative filtering method based on contextualized implicit relationships among items. Background technique [0002] In recent years, with the increasingly serious problem of Internet information overload, the provision of many services urgently needs the support of personalized recommendation systems. However, traditional recommendation techniques only consider two entities, namely "user" and "item", while ignoring the impact of contextual information (such as time, location, person, activity status, device status, network condition, etc.) on recommendation. For this reason, context-aware recommendation systems have gradually attracted attention. Practice has proved that the introduction of context information can effectively improve the accuracy and personalization of recommendations. [0003] At present, context-aware recommendation algorithms are mainly divided into ...

AI Technical Summary

Problems solved by technology

[0004] The above context-aware recommendation algorithm has the following deficiencies: (1) Data sparsity problem: With the introduction of context information, the three-dimensional user-item-scenario scoring matrix is ​​more sparse than the traditional user-item scoring matrix, and context-related pre-filtering and The recommended method of post-filtering is difficult to achieve the desired accuracy

(2) Scalability issues

Although the context-related modeling method is more suitable for sparse data than the context-related pre-filtering method and post-filtering method, the existing scene-related modeling methods often have high computational complexity and poor scalability.

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