Recommenders

This package aims to provide light-weight recommendation models, mainly for implicit feedback data. We want to provide

• consistent interface for model training and inference
• flexibility for input data with Tables.jl package, which offers simple, but powerful abstract interface for tabular data
• robust baseline metrics for classic datasets. The comparison of advanced recommendation models to these baselines turns out to be challenge [1, 2].

See Getting started for quick start. More advanced usage is scripted in examples.

[1]: M. F. Dacrema et. al., Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches

[2]: S. Rendle, Evaluation Metrics for Item Recommendation under Sampling