With growing volumes and varieties of available data, businesses are increasingly keen to extract valuable information so they can use that knowledge to optimise decision-making and develop strategies which support customer engagement.
This is of particular relevance to marketing professionals, with many organisations seeking to improve customer relationship management through the increasing popularity of location-based social networks (LBSNs), such as Foursquare, GyPSii and Loopt, and how they encourage more and more users to share their experience for point-of-interest (POI) in a cyber world.
When users visit a POI they post their physical locations, comments and tips that compose a set of check-in data in the registered LBSNs. The quick aggregation of data naturally generates valuable service of POI recommendation that instructs users on exploring new places.
The technology behind these data extraction innovations is known as machine learning, which I’ve been researching for the past 10 years. Machine learning has been used to develop driverless cars and effective web search among many other innovations, and is the science of getting computers to act without being explicitly programmed. It is believed by many researchers to be the best way to make progress towards human-level artificial intelligence.
Extracting valuable information from data often resorts to machine learning techniques. Machine learning algorithms can automate the data analysis process by applying complex mathematical models to discover hidden insights from the data, and are core elements in big data analytics.
My research focuses on improving the POI recommendation by considering location categories, such as shops, restaurants and heritage landmarks, so that the recommended POIs enjoy a large diversity on the categories. This will enhance the capability of recommender systems on providing personal recommendation service.
In addition, through my work I have been able to extend the POI recommendation to the route planning that is developed from the collection of data in LBSNs. The new route planning system configures the recommended routes by considering users’ preference and satisfies their requirements.
My research so far has been conducted in real-world datasets collected from popularly-used LBSNs – and the application of the technology works. I am able to provide this expertise to potential collaborators in sectors where the research could be deployed to generate new forms of customer engagement for organisations.
By Yifeng Zeng, reader in the School of Computing at Teesside University
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