Cross recommendation method and system based on local weighted linear regression model

Abstract

The invention provides a cross recommendation method and system based on a local weighted linear regression model. The cross recommendation method comprises the following steps: obtaining a scoring record of a user for at least one target item in a target object and an auxiliary scoring record for at least one auxiliary item in an auxiliary object related to the target object; establishing a to-be-evaluated project vector of the to-be-evaluated target project according to the scoring record and the auxiliary scoring record; expanding the vector of the project to be evaluated into an augmentedvector, and establishing a local weighted linear regression model according to the augmented vector; solving the local weighted linear regression model by utilizing a random gradient descent algorithmto obtain an optimized solution; calculating a prediction score of a to-be-evaluated target project in the target object according to the optimization solution; and recommending the to-be-evaluated target project with the predicted score meeting a preset score standard to the user. By implementing the method and the device, the behavior data and the score data of the user are fully fitted, so that the accuracy and the recall rate of the cross recommendation system are improved.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a cross-recommendation method and system based on a locally weighted linear regression model. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. In this era, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find the information they are interested in from a large amount of information; It is also very difficult for the information produced by oneself to stand out and attract the attention of the majority of users. Recommender systems are an important tool to resolve this contradiction. The task of the recommendation system is to connect users and information. On the one hand, it helps users find information that is valuable to them, ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the cross-recommendation algorithm based on the quadratic regression model in the prior art. Since the relationship between user behavior data and user rating data is often extremely complicated, it is difficult for the quadratic regression model to fully understand it. Fitting (that is, the data is in a state of under-learning), thus affecting the accuracy and recall of the cross-recommendation system; and establishing a higher-order (such as three, four) regression model may be more adequate for data fitting, but it is easy Causes overfitting and also affects the accuracy and recall of the cross-recommendation system

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