E-commerce platforms often face challenges in delivering personalized recommendations to enhance customer engagement and conversion rates. In collaboration with Royal Cyber, this project develops an end-to-end machine learning pipeline on Databricks integrating structured session data and unstructured textual reviews to create scalable and accurate product recommendation systems. Employing collaborative filtering, content-based filtering, and product embeddings, the project aims to significantly improve recommendation quality. By utilizing customer engagement metrics as key outcome variables, the solution demonstrates measurable improvements in personalization and performance.
Keywords: E-commerce, Machine Learning, Personalized Recommendations, Databricks, Collaborative Filtering, Content-Based Filtering, Delta Lake
Watch the team present this project in the session recording here.
Faculty Advisor
Dr. Ali is a seasoned leader in data and analytics, ascending from an engineer role to senior leadership. He has successfully led, designed, and built some of the largest global data solutions across diverse industries such as telecom, financial services, insurance, CPG, Retail, and manufacturing. His expertise spans leadership, product/program management, data engineering, data architecture, data security, AI/ML ethics, and privacy.
His successful global career includes roles across Asia, the UK, EU, MEA, APAC, and the USA. He gained extensive experience at major organizations such as NCR, Teradata, Barclays Bank, Capgemini, and Circana (IRi). His client portfolio is extensive, featuring top global companies like Telenor, Vodafone, Morgan Stanley, Citigroup, The Hartford, CNA, Altria Group, Walmart, and Apple. After working at large corporations, he transitioned to the start-up ecosystem and worked at two AI unicorns i.e. Dataiku and Sigma Computing and later co-founded two AI start-ups i.e. Realix.ai and Digital Emissions.
He now focuses on education and paying forward. He teaches in graduate data science programs and is a regular speaker on data and AI panels and a frequent guest lecturer. He remains active in the entrepreneurial space, advising early-stage start-ups on product innovation, leadership, culture, customer success, and growth strategies. He is a perpetual learner and holds multiple advanced degrees and continues to learn every day.