Sparse Linear Methods (SLIM) Recommender with two optimization techniques

The SLIM algorithm generates efficient recommendations and its performance is shown in the original paper (http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf). The study demonstrates that SLIM outperforms traditional algorithms (such as itemkNN, userkNN, SVD or other Matrix Factorization approaches) on various data-sets in terms of time efficiency and recommendation quality.
SLIM's optimization problem can be solved using Least Square optimization (designed for explicit feedback datasets) and Bayesian Personalized Ranking (designed for implicit feedback datasets).

Although this implementation started when Mahout still had the "core" module (therefore this code might be a little "late to the party", since core is now mrlegacy), maybe this package can be useful to Mahout Taste users that seek ways to improve their recommenders.