This week we are joined by Susan Craw from RGU, who is going to be explaining how online recommendations are made – how exactly does Netflix know which films you’d like to watch, how does Amazon know what other products you might like?
Millions of people use online services every day. With so many options, recommender systems are becoming increasingly important to help us make choices: which camera, computer, washing machine meets our needs; which clothing, furniture, fabrics fits our preferences; which book, movie, music suits our taste; which destinations, hotels, activities match our vacation wishes. Recommenders for online serv…ices such as Amazon, Netflix, Spotify, Tripadvisor, … help us to make choices by presenting us with suggestions that may suit. But how do they do it?
Susan Craw has been applying Artificial Intelligence to build smart information systems throughout her career at RGU. She is interested in extracting knowledge from sources of ‘big data’ including online document, music and image collections. Smart systems have been developed for Pharmaceutical product design and Oil & Gas decision support. Recent apps help people engage with information – recommending on-line music, browsing textile archives, and context-aware interpretations for museum exhibits and historic sites.