Information recommendation method based on contextual ontology tree to calculate user preference similarity

Abstract

The invention discloses an information recommendation method of calculating user preference similarity based on a context ontology tree. According to the method, differential influences of various context types and concrete examples thereof on user preferences are analyzed, and a context-based user preference extraction method is designed; then ontology is utilized to carry semantic expression on contexts, an ontology tree expression-based context similarity calculation method is constructed, and the precision of finding similar user sets in collaborative filtering recommendation is improved; and on the basis of the above-mentioned user preference extraction, the extracted contexts are introduced into a collaborative filtering recommendation process, the similarity between the context user preferences is calculated, a collaborative filtering recommendation method based on user context preference analysis is designed, and problems of sparseness, cold starting and the like of existing recommendation methods are solved.

Description

technical field [0001] The invention relates to the field of personalized recommendation in smart commerce, in particular to an information recommendation method for calculating user preference similarity based on a context ontology tree. It is especially suitable for the product recommendation of group users whose product rating data is sparse and affected by complex contexts in online transactions of smart shopping malls. The recommendation technology that incorporates contextual preference information can accurately and efficiently provide personalized services that not only conform to the user's internal and external context, but also meet user preferences. [0002] technical background [0003] The development of technologies such as mobile communication networks and intelligent information processing has met the diverse needs of users for Internet goods or services, enabling users to obtain preference information in a timely manner through mobile smart terminals. Howev...

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Problems solved by technology

[0005] In order to overcome the existing methods in the personalized recommendation process, the user scoring technology is too one-sided, and the reasons for the differences in user preferences in different types of contexts are rarely analyzed, and there are problems such as insufficient semantic expression of multi-dimensional contexts, sparsity, cold start, etc., the present invention provides An Information Recommendation Method Based on Context Ontology Tree Computing Similarity of User Preferences Based on Context and Score Information

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