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Private and Open Data in Asia: A Regional Guide.
Recommenders are ubiquitous in the e-commerce space. You see them on websites under “People who have bought this also bought…”. You receive daily emails with titles like “Handpicked new products for you!” The unique thing about recommenders is that they do not have a clear “correct answer”. For example, recommending me another smartphone after I have just bought one is “correct” based on my purchase behavior but really a poor choice in terms of context. On the other hand, keeping me updated on the power bank I have in my wish-list and recommending me similar but cheaper alternatives might show several “wrong” items that I will never buy, but it might also help me discover new, better products than what I had in mind.
For this talk, I will share what my team at Lazada has learnt from building the largest e-commerce recommender in South East Asia:
Kai Xin Thia is a data scientist at Lazada. He specializes in behavioral analytics and has an interest in large recommendation systems. He has been building behavioral models for three years and is in the top 1% on Kaggle, an international data science competition portal. Kai Xin is also the co-founder of DataScience SG (the largest data science community in Singapore), volunteer at DataKind SG (NGO that helps other NGOs through data science), and is an invited speaker/trainer at various data meetups in Singapore. He likes traveling and experiencing the diversity of the world.
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