Julia's ecosystem provides access to Recommendation.jl, a package that implements a multitude of algorithms for both personalized and non-personalized recommendations. For model-based recommenders, it has support for SVD, MF, and content-based recommendations using TF-IDF scoring algorithms.
There's also another very good alternative—the ScikitLearn.jl package (https://github.com/cstjean/ScikitLearn.jl). This implements Python's very popular scikit-learn interface and algorithms in Julia, supporting both models from the Julia ecosystem and those of the scikit-learn library (via PyCall.jl). The Scikit website and documentation can be found at http://scikit-learn.org/stable/. It is very powerful and definitely worth keeping in mind, especially for building highly efficient recommenders for production usage. For learning purposes, we'll stick to Recommendation, as it provides for a simpler implementation.