A context automatic coding recommendation method and system for complete cold start

Abstract

The invention discloses a context automatic coding recommendation method and system for complete cold start, and the method comprises the steps: carrying out the coding of all kinds of context information of an old user, enabling the coded coding vectors to be connected in series, enabling the coding vectors to serve as input vectors, and enabling the input vectors to be input into a context perception recommender; allowing a context awareness recommender to set a local matrix for each neighbor of each old user for expressing correlation between the context-feedback information, enabling the feedback information of each old user to be obtained through calculation according to the user file and the local matrix; And finally, according to the file correlation degree between the new user andthe old user, calculating n neighbor old users corresponding to the new user, constraining feedback information of the old user through file correlation of the new user and the old user, calculating feedback information of the new user, and obtaining a cold start recommendation list of the new user. By adopting the embodiment of the invention, the context association relationship between new and old users can be mined, the new user recommendation problem is solved, and the accuracy of complete cold start recommendation is improved.

Description

technical field [0001] The invention relates to the field of recommendation systems, in particular to a completely cold start-oriented context automatic coding recommendation method and system. Background technique [0002] In recommender systems, the cold-start problem is caused by missing user actions (such as clicks, ratings, likes, etc.). Specifically, the cold start problem can be divided into complete cold start and incomplete cold start. It is customary to call the complete cold start problem the "new user problem" because the new user has not performed any behavior in the system. [0003] Existing solutions to new user problems are mainly to collect additional information to assist in recommendation, such as conducting user questionnaires or obtaining user profiles and item attributes before recommendation. This additional information is called context (Context), and using contextual information to make recommendations is called context-aware recommendation, such as...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention proposes a complete cold start-oriented context automatic coding recommendation method and system, which can mine the contextual relationship between new and old users, solve the problem of new user recommendation, and improve the accuracy of complete cold start recommendation

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