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• W2952585906 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." @default.
• W2952585906 created "2019-06-27" @default.
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• W2952585906 date "2019-03-24" @default.
• W2952585906 modified "2023-10-17" @default.
• W2952585906 title "Deep recommender engine based on efficient product embeddings neural pipeline" @default.
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• W2952585906 hasPublicationYear "2019" @default.
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