Recommendation method based on situation fusion sensing

Abstract

The invention provides a recommendation method based on situation fusion sensing. The recommendation method comprises the following steps that 1, the situation is divided into physical situations and user preference situations according to the definition and the requirements of the situations; 2, a Bayes network is built through parameter learning and structure learning, and the physical situation matching degree in a certain environment is ratiocinated and calculated; 3, through considering the dynamics of hobbies and interests of users along with the time change, a time function is merged into a recommendation algorithm based on the content, and the matching degree of the user preference situation is calculated; 4, the situation matching degree is comprehensively considered, all candidate information resources are graded, and in addition, information ranking in first Top-N is recommended to target users. Compared with the prior art, the recommendation method provided by the invention has the advantages that the considered recommendation factors are more comprehensive, the method can better adapt to changeful environment, the recommendation accuracy is improved, in addition, the condition that the interest of the users is changed along with the time change is considered, the time function is combined with the recommendation based on the resource content, and the user satisfaction degree is improved.

Description

technical field [0001] The invention relates to the field of network information technology, in particular to a recommendation method based on context fusion perception. Background technique [0002] The rapid development of the Internet and the ever-increasing information resources have led to a sharp increase in the information index. The information service field is facing the problem of "rich information resources, but difficult to obtain useful information", which brings a great information burden to people. On the one hand, a large number of data resources appear on the network, leading to the phenomenon of "information overload" (information overload); on the other hand, users cannot obtain the information resources they need. Recommendation systems (recommendation systems), as an important method of serving in the "information push" mode, are the main means to solve the problem of information overload. For information that is difficult to obtain, recommend more valu...

AI Technical Summary

Problems solved by technology

[0005] At present, the most typical application of personalized recommendation systems is in the field of e-commerce, but these recommendation systems and corresponding algorithms only focus on the two-dimensional relationship between users and information, and seldom consider the environment in which users live (such as time, geography, etc.). Location, surrounding people, emotions, behaviors, network conditions, etc.) resulting in low-precision recommendations

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