Many retailers and e-commerce operators (including online platforms) sell products from large and frequently changing assortments, such as sellers of clothes, footwear, bags, fashion accessories, video games, music, movies, books, electronics, and gadgets.

Inferring consumer preference structures in such context is essential for making a variety of marketing actions such as making merchandising decisions and offering personalised shopping recommendations. Yet, it is a very challenging task due to the large and varying nature of the merchandise. In addition, traditional modelling approaches developed in the marketing literature are not well equipped to handle the data.

This requires a new scalable modelling framework of discovering consumers’ preference structures in large and frequently changing assortments at the store/marketplace level. I developed a scalable stock-keeping-unit-level modelling framework of discovering consumers’ preference structures in large and frequently changing assortments at the store/marketplace level. My research proposes a personalised product recommendation system based on individuals’ preference inferred from my model.