Time perception service recommendation system and method based on self-attention factor decomposition machine

Abstract

The invention provides a time perception service recommendation system and method based on a self-attention factor decomposition machine, and the system comprises a PFM model which inputs a user hiding vector, a service hiding vector and a time hiding vector, input through an input layer, into an FM module for processing, and outputs the processed data to an output layer through a first full connection layer and a first activation function of a middle layer in sequence; and an SAGRU model which inputs the embedded vectors of the user and the service set on the time interval t input through theinput layer into the GRU module for processing, and outputs the processed data to the output layer through the self-attention mechanism unit, the second full connection layer and the second activation function of the middle layer in sequence. Compared with a matrix decomposition technology, the method has the advantages that the nonlinear relationship between the user and the service can be effectively learned, the dynamic behavior characteristics of the user along with time change can be captured, and the problem of service quality data sparseness in the real world can be effectively relieved.

Description

technical field [0001] The invention relates to the technical field of recommendation systems, in particular to a time-aware service recommendation system and method based on a self-attention factorization machine. Background technique [0002] Recommending personalized services that meet user needs in a series of services with similar functions is called service recommendation. The traditional method mainly uses collaborative filtering technology, which can be divided into content-based collaborative filtering algorithm and model-based collaborative filtering algorithm. [0003] The content-based collaborative filtering algorithm mainly predicts the QoS value based on complete data. Such methods can be divided into three types according to the category of content: user similarity-based, item-based similarity, and hybrid similarity-based methods. Jin et al. have proposed a domain-aware deep learning method to predict the QoS value of web services. First, he uses the Pierr...

AI Technical Summary

Problems solved by technology

Although it can improve the recommendation performance in some aspects, there are still two problems: the first is the cold start problem, when new users and data are encountered, the data cannot be updated in time

The second is the problem of data sparsity

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