Data mining and personalized recommendation for an online luxury fashion retailer

The September is an online luxury retailer. In this research project, we are going to design and implement a personalized recommender system to enhance customers’ online shopping experiences. The past transaction data and browsing history as well as the demographic information of customers will be analyzed to identify the purchasing patterns, shopping trends and user behaviors. Based on these mined patterns and generated user profiles, recommendations will be made to individual customers using state-of-the-art recommendation algorithms. Through the built recommender system, the shopping experiences of the September’s customers can be greatly enhanced, and potentially it could help the company attract new customers and retain existing customers.

Faculty Supervisor:

Chen Ding

Student:

Omar Nada

Partner:

September Inc

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

Ryerson University