Revolutionising online personalisation with smarter data mining and analytics

With the ever-growing amount of data generated through multiple sources of user activities, there is more information to learn about consumers’ personal preferences, but also a rising hurdle to do this more intelligently.

Understanding more about consumer behaviour and preferences, and offering better product and service recommendations, can allow digitally-oriented organisations to deliver more personalised experiences or services.  Consumers will find more easily what they are looking for, while businesses will enjoy higher sales and customer brand loyalty.  These can become critical advantages in the competitive and crowded marketplace.

At the core of this technology are algorithms that can crunch a large amount of data to learn about user preferences.  With an increasing volume and variety of data, there must also be revolutionary ways for researchers to sift through information and develop more efficient methods to derive useful real-world benefits and results.

SMU Assistant Professor Hady Lauw from the School of Information Systems (SIS) has been studying algorithms so as learn the preferences of consumers from large-scale data, and enable targeted data mining solutions for more accurate personalisation in e-commerce applications.

In recognition of his research progress and future potential, Asst Prof Lauw recently became the first SMU faculty member to be awarded the prestigious Singapore National Research Foundation (NRF) Fellowship.  He will be conducting a five-year research on data mining and machine learning, with a focus on dimensionality reduction technologies for recommender systems.  He will continue to develop personalisation and recommendation technologies, and address important questions in data mining and machine learning.