Support vector regression recommendation method and support vector regression recommendation system based on context sensing

Abstract

The invention discloses a support vector regression recommendation method based on context sensing. The method comprises the following steps: constructing a user characteristic attribute matrix; obtaining an item characteristic attribute matrix and an item characteristic attribute preference matrix, and constructing a user preference matrix; constructing a context situation matrix and a grading matrix, and constructing a context-based user preference model according to the context situation matrix, the user characteristic attribute matrix, the user preference matrix and the grading matrix; optimizing the context-based user preference model by virtue of a support vector regression SVR algorithm, so as to obtain an effective grading prediction model; and calculating the grades of items which are not bought by a target user based on the grading prediction model, and recommending front L items with the highest grades to the target user. The invention further discloses a corresponding system. The method and the system can be applied to the recommendation of supplementary services of civil aviation passengers, and services suitable for the passengers can be rapidly and accurately found from numerous services.

Description

technical field [0001] The invention relates to the technical field of civil aviation, in particular to a context-aware support vector regression recommendation method and system. Background technique [0002] With the continuous development of the aviation industry and the increasing number of service types, passengers face a variety of services and cannot quickly find the services they need. However, the services and service information recommended by existing airlines for passengers cannot meet the needs of users and cannot In the shortest possible time more targeted to provide passengers with suitable services. [0003] At present, there are some methods for recommending services for users, which can be based on collaborative filtering algorithms, content-based recommendation algorithms, hybrid recommendation algorithms and other recommendation algorithms. Among them, the core idea of ​​the collaborative filtering recommendation algorithm can be divided into two parts: ...

AI Technical Summary

Problems solved by technology

[0002] With the continuous development of the aviation industry and the increasing number of service types, passengers face a variety of services and cannot quickly find the services they need. However, the services and service information recommended by existing airlines for passengers cannot meet the needs of users and cannot Provide passengers with more targeted services in the shortest possible time

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