Lenskit: Precision, Recall and NDCG

This post is dedicated to accuracy metrics in Lenskit framework.

Lenskit: Recommendation browser

Recommendation Browser has been developed to investigate recommendations users receive in Lenskit framework. The graphical user interface looks as follows:

Lenskit: Popular items evaluation exception solution

In Lenskit 2.2.1 in evaluation when algorithms get to rank popular items the framework throws exception. The problem has been reported to the authors of Lenskit and it will be fixed soon. In this post I offer a workaround for this problem for those who use version 2.2.1.

Lenskit: Popularity baseline (Learning to Rank)

This post is dedicated to popularity baseline in Lenskit 2 framework. The framework lacks this baseline. I therefore provide an implementation and demonstrate the results of the baseline.
The implementation includes three classes:
PopItemScorer - items scorer, which provides actual scores for items
PopModel - model that contains popularity of each item
PopModelBuilder - builder calculates popularity for each items and puts them to the model.

Lenskit code example

Recently, I started using Lenskit framework. The framework is designed for recommendations. It contains a few useful recommendation algorithms, such as item-item collaborative filtering and matrix factorization. However, there is a lack of documentation and examples on the framework.
I needed to use SimpleEvaluator class and I could not find a relevant documentation on the the class or a good example how I can use it.