The purpose of this project is to develop a highly accurate e-commerce recommender system able to select products across databases and recommend them to prospective customers both in real-time and off-line. Leveraging the historical inventory of sold products, browsing history, purchase history, and expressed preferences helps the recommender to formulate highly accurate product suggestions to find closest matches to what a consumer is looking for. Consumers will instantly be shown other products similar to what she/he is looking at as well as an assortment of other products that complement it. For example, a prospective customer looking at blouses of a certain type will be shown other blouses of the same look and style as well as other well-matched pants, bracelets, earrings and purse.