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.

Comment: 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet)


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The different versions of the original document can be found in:

http://dx.doi.org/10.1109/roedunet.2018.8514141
https://academic.microsoft.com/#/detail/2898744257
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/roedunet.2018.8514141
Licence: CC BY-NC-SA license

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