Context-aware Support Vector Regression Recommendation Method and System

Abstract

The invention discloses a context-aware support vector regression recommendation method, which includes: constructing a user characteristic attribute matrix; obtaining an item characteristic attribute information matrix, obtaining a preference matrix of item characteristic attributes, and constructing a user preference matrix; constructing a contextual situation matrix, and constructing Rating matrix, based on the context matrix, user characteristic attribute matrix, user preference matrix and the rating matrix, a context-based user preference model is constructed; the support vector regression SVR algorithm is used to optimize the context-based user preference model to obtain An effective rating prediction model; calculate the ratings of the target user's unpurchased items based on the rating prediction model, and recommend the top L items with the highest ratings to the target user. The invention also discloses a corresponding system. The invention can be applied to the recommendation of additional services for civil aviation passengers, and can help passengers quickly and accurately find services suitable for themselves among 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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