  Product recommender systems and customer profiling techniques have always
been a priority in online retail. Recent machine learning research advances and
also wide availability of massive parallel numerical computing has enabled
various approaches and directions of recommender systems advancement. Worth to
mention is the fact that in past years multiple traditional "offline" retail
business are gearing more and more towards employing inferential and even
predictive analytics both to stock-related problems such as predictive
replenishment but also to enrich customer interaction experience. One of the
most important areas of recommender systems research and development is that of
Deep Learning based models which employ representational learning to model
consumer behavioral patterns. Current state of the art in Deep Learning based
recommender systems uses multiple approaches ranging from already classical
methods such as the ones based on learning product representation vector, to
recurrent analysis of customer transactional time-series and up to generative
models based on adversarial training. Each of these methods has multiple
advantages and inherent weaknesses such as inability of understanding the
actual user-journey, ability to propose only single product recommendation or
top-k product recommendations without prediction of actual next-best-offer. In
our work we will present a new and innovative architectural approach of
applying state-of-the-art hierarchical multi-module encoder-decoder
architecture in order to solve several of current state-of-the-art recommender
systems issues. Our approach will also produce by-products such as product
need-based segmentation and customer behavioral segmentation - all in an
end-to-end trainable approach. Finally, we will present a couple methods that
solve known retail & distribution pain-points based on the proposed
architecture.
