Content recommendation system and method fusing information age and dynamic graph neural network

Abstract

The invention discloses a content recommendation system and method fusing information age and a dynamic graph neural network. The system comprises a time feature learning module, a structural feature learning module and a preference calculation module. The time feature learning module is used for extracting time information from historical request data of each user, screening available information based on information age, and finishing long-term feature extraction operation by setting a multi-head attention mechanism and a feedforward network and gating circulation unit; the structural feature learning module obtains a node embedding representation containing time features and structural features by using a graph attention mechanism; and the preference calculation module uses a multilayer perceptron to calculate the preference degree represented by the node embedding output by the structural feature learning module. According to the method, the selection and aggregation work of the historical information is completed by using the attention mechanism of the fusion information age, the time characteristics contained in the historical information are fully extracted, and the overall performance of the network is improved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a content recommendation system and method for integrating information age and dynamic graph neural network. Background technique [0002] With the development of network technology and the rapid growth of network equipment, content data represented by video and AI data for artificial intelligence services will experience explosive growth. The surge in data volume inevitably leads to an increase in transmission delay. In order to meet people's demand for low-latency services to the greatest extent, emerging network architectures such as information-centric networks propose to cache some popular content at the edge of the network near the user side. . How to accurately filter out the content worthy of being cached among the huge amount of content is our primary problem to solve. Traditional caching algorithms and some predictive caching strategies base...

AI Technical Summary

Problems solved by technology

[0003] Among the existing cache prediction algorithms for content, the most common strategy is to use the LRU (least recently used, least recently used) or LFU (least frequently used, most recently used) algorithm, all of which obtain data requests through statistical analysis Simple rules; while some popular prediction schemes based on cyclic neural network can fully learn the time characteristics of data, but ignore the structural relationship between users; interaction, but it is still difficult to simultaneously extract temporal features

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