Recommendation system for retail shopping

People rely on recommendations from other people, friends’ word, news reports, and travel guide and so forth. Recommendation systems assist people to sift through available books, web pages, restaurants, and grocery products. [16]. We want to build a recommendation flow in the retail industry to serve Canadian citizens better. The system will understand the customers and help them to make better selections and improve their shopping experience. A retail recommendation is different from e-commerce as the basket is substantially larger and customer tends to buy same product over and over again. In this project to build models to understand the existing customer base and products for shopping suggestions, robust substitutions, and search ranking. The system will make recommendations base on the users that are similar. For example, the system will learn your shopping behaviours and make product recommendation based on purchased history of other users that share the similar shopping behaviours.

Faculty Supervisor:

Nick Koudas


Wei Zheng


Loblaw Digital


Computer science


Information and communications technologies




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