Learning a generalized matrix from multi-graphs topologies towards microservices recommendations



This paper presents a methodology that combines latent factor models with graph-based models. The proposed recommendation system identifies a recommended item as a node of a graph. More specifically, the topology of the graph and the paths between the nodes are considered as critical features regarding the associations between them. Furthermore, in the current approach, these structural features are considered as feedback. These structural features are extracted from a pool of several application graphs which are afterwards generalized into a unified matrix of proximities. The main reason for the use of this structural feedback is to generate recommendations and discover unobserved relations using matrix factorization techniques. The approach is tested on a data set that consists of cloud-native microservices graphs.

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