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【Speaker Highlight】Ming Cheung, Research Scientist @Beta Lab, Lane Crawford Joyce Group

What are the strengths of Lane Crawford Joyce Group's product recommendation system?


As part of ecommerce personalization, product recommendations are displayed on a web page, app, or email based on various variables, including customer attributes, browsing history, or situational context, which enables a unique shopping experience. For organizations with a large and diverse product catalog, product recommendations are particularly valuable. To build a high-quality product recommendation engine, what kinds of data are needed? Here is Beta Lab's Research Scientist - Ming Cheung's experience and insights!

LCJ group has stored so many purchasing information. Also, we are able to talk to different business users. Under the same umbrella, we can obtain a great deal of insider information, such as merchandiser preferences, how they plan to buy products for the upcoming season, stock inventories and warehouse data etc. Making LCJ's product recommendations and machine learning modal very successful and well-customised.

Ming Cheung

Research Scientist

Beta Lab, Lane Crawford Joyce Group


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