Recommender systems in Retail 2019: Overview and Use case

Recommender systems in Retail 2019: Overview and Use case

 A Retail Case study about implementing recommender systems developed during the Summer School of Research Methods In summer 2019, some of the ShopUp team took part in the Summer School of Research Methods. The 7-day event was organized by several members of Data Science Society and academia representative from Sofia University, UNWE and Technical University Sofia. There were more than 10 lecturers and about 30 participants from different companies, organizations and universities. Among them were experts, researchers, PhD candidates and masters students. Each day the program started with presentations or workshops followed by allocated time for a Capstone project (a project which aims to capture what we’ve learnt). The workshops combined practice and theory in the area of maths, statistics, neural network, reinforcement learning and etc. The Capstone project was mandatory for each participant and there were three different cases to select from.  We at ShopUp decided to open-source this project work and share it with the community. The code we created is implementing different recommenders’ techniques for building a retail recommender engine using data, provided from a Kaggle contest.  What Is a Recommender System? A recommender system (or a recommendation system) can be perceived as a black box, offering different items to end users, depending on their past interest and behaviour, no matter if the user is a retailer, a store, a shopping or entertainment center. The more relevant items are offered, the higher interest and revenue is generated. Therefore for marketing and sales purposes the higher prediction rate means higher ROI in promoting different products. A relevant example is: if you buy horror books, the engine would offer you a book which... read more
ShopUp is Now Scalable

ShopUp is Now Scalable

In the last 2 months we’ve done a lot of progress with our product and business development. We improved the performance of the ShopUp sensors by upgrading the firmware. The amount of data we’re now gathering have increased by more than 5 times. Also, we found a way to install the firmware directly on customers’ routers. We support more than 1,000 devices. That means we are now able to launch ShopUp remotely, without the need of physical installation on site within less than 24 hours. That is a huge advantage in terms of scalability.

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