This paper investigates the rating of network paths, i.e., acquiring quantized measures of path properties such as round-trip time and available bandwidth. Compared to fine-grained measurements, coarsegrained ratings are appealing in that they are not only informative but also cheap to obtain. Motivated by this insight, we first address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to a recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. A technical contribution is an active and progressive inference framework that not only improves the accuracy by selectively measuring more informative paths, but also speeds up the convergence for available bandwidth by incorporating its measurement methodology. Then, we investigate the usability of ratingbased network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer
selection and that finer granularities do not always lead to larger improvements.