Introduction

5GTANGO's Recommendation engine is part of the SONATA powered by 5GTANGO Validation and Verification Platform (V&V). The tng-vnv-dsm repository includes the source code for building and running a python based application (running Flask framework on top - providing restful end points). The corresponding application aims at providing a set of recommendation tests to the end users, when they choose to trigger a test for a specific NS. Also, this repository includes a .README describing the recommendation engine, and how you can locally install and play with it.

Collaborative Filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as tests without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, singular-value decomposition (SVD) approach. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past.

Singular-Value Decomposition (SVD) In linear algebra, the singular-value decomposition (SVD) is a factorization of a real or complex matrix. It is the generalization of the eigendecomposition of a positive semidefinite normal matrix (for example, a symmetric matrix with positive eigenvalues) to any m x n matrix via an extension of the polar decomposition. It has many useful applications in signal processing and statistics.