Modulai has, in collaboration with Lindex, developed and deployed a custom-built recommender system. Lindex is dedicated to offer their customers a relevant and transparent personalized experience, in a multi-channel context. To be able to deliver on that front, a robust recommender system is considered a vital cornerstone.
Background
Lindex is a major retailer, active in the Nordics and throughout Europe. They have several million registered customers and are one of the prominent brands in women’s wear, lingerie, and kidswear. Nowadays, users expect retailers to provide them with recommendations based on what they’ve clicked on and bought. A feature that both generates more sales and provides the e-shopper with a sense of being cared for.
Solution
Data on approximately 60000 products and purchases from over 2 million customers was used. Customers were characterized using their historical transactions. Various sets of data were considered for feature extraction, including multiple levels of product categories, product attributes such as color, texture and material. Natural language data was included and processed using various classical NLP techniques, Universal Sentence Encoders, FastText and BERT. Image data was transformed into dense representations using ResNet and convolutional autoencoders were included in the modeling. Recommendations were produced on an individual level using both matrix factorization and gradient boosting models.
Tools/Tech
The project used a stack of relational and time-series databases for data storage and retrieval as well as Python, Tensorflow ,and Scikit-learn for modeling.