LensKit for Python 0.6.0 released

[Michael Ekstrand]

Over the weekend, I released LensKit 0.6.0. Release notes are here: https://github.com/lenskit/lkpy/releases/tag/0.6.0

The big improvements here are to improve and stabilize top-N recommendation and evaluation APIs.  We now have a 'candidate selector' component, and the top-N recommender implementation uses an 'unrated items' candidate selector by default. This means you can now ask for recommendations without needing to specify a candidate list yourself - it will memorize users' rated items from data passed to 'fit'. Consequence: you need to 'adapt' a predictor into a recommender before you call 'fit' to train it.

We also have the new RecListAnalysis API that makes it easy to correctly compute top-N evaluation metrics. It was possible to compute nDCG before, but it was difficult to so correctly; computing recall required a lot of manual work. Now that we have more internal experience with the APIs, we have settled on this to make it straightforward to compute a wide range of top-N metrics. Pull requests are welcome for new metrics!

Enjoy :)

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Michael D. Ekstrand — michael...@boisestate.edu — https://md.ekstrandom.net
Assistant Professor, Dept. of Computer Science, Boise State University

People and Information Research Team (PIReT) — http://coen.boisestate.edu/piret/