A Collaborative Filtering Recommendation Method Based on Optimal Extraction of Elastic Dimension Feature Vectors

Abstract

The invention provides a collaborative filtering recommendation method based on elastic dimensional feature vector optimized extraction, and belongs to the technical field of Internet information recommendation. The recommendation method is constructed by using user feature vectors and recommendation object feature vectors, and dimensions in which a user is interested and to which a recommendation object really belongs in each user feature vector and each recommendation object feature vector are elastically obtained respectively by using user assistant vectors and recommendation object assistant vectors. With no professional knowledge and individual information, the collaborative filtering recommendation method is secure and simple; the minimum root-mean-square error is adopted as an optimization constrain condition; in an implementing process, only existing parts in a rating matrix are constrained, but a correct fitting mark can be also made, and the problems of data sparseness and cold starting caused by lack of historical data are solved. The method can be used for obtaining the dimensions which really work in each user feature vector and each recommendation object feature vector, and adaptively adjusting the search direction, so that overfitting of the recommendation method is avoided, and a recommendation result is optimized.

Description

technical field [0001] The invention belongs to the technical field of Internet information recommendation, and in particular relates to a collaborative filtering recommendation method based on optimal extraction of elastic dimension feature vectors. Background technique [0002] The popularity and development of the Internet has brought a large amount of information to users. While meeting the needs of users for information in the information age, it has also brought about the problem of information overload. One of the effective solutions to the problem of information overload is a personalized recommendation system. The recommendation system discovers the user's points of interest, thereby guiding the user to discover their own information needs. Personalized recommendation systems are widely used in many fields, especially in the field of e-commerce. In academia, recommender systems have gradually become an independent discipline. The recommendation method is the core ...

AI Technical Summary

Problems solved by technology

The main disadvantage is that although there are many recommended combination methods in theory, the scalability of this system is not good, and there is no standardized and exact method for solving the problem;

[0016] As an effective means of information filtering, the personalized recommendation system is one of the effective methods to solve the problem of information overload and realize personalized information services; however, with the explosive growth of the number of users and the number of services, the high dynamic And the continuous complexity of the data generated by the service makes the service-oriented recommendation system have to face some new challenges, especially the high-dimensional problem of data characteristics, the problem of data coefficient and the problem of cold start

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