Deep recommender engine based on efficient product embeddings neural pipeline

TL;DR

This paper presents a deep neural pipeline for product recommendation tasks, combining unsupervised and supervised models to predict product relationships and sales using real-world pharma retail data.

Contribution

It introduces a hybrid, parallelized pipeline integrating semantic product embeddings with sales prediction for enhanced recommendation accuracy.

Findings

Effective multi-task learning approach demonstrated.

Benchmarking on pharma retail data shows promising results.

Hybrid model improves recommendation quality.

Abstract

Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper, we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid model pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking processes have been done using pharma industry retail real-life transactional Big-Data streams.