Close
Student creates impact on viewer recommendation systems, with BBC R&D
31 July 2023
An Electronic and Electrical Engineering student鈥檚 BEng project on improving recommendation systems using a novel machine learning algorithm, has been featured on the BBC R&D website and led to a paper accepted within an Association for Computing Machinery (ACM) conference.
Electronic and Electrical Engineering undergraduate student, Iason Chaimalas鈥� recently completed BEng project has been recognised for its impact, being featured on the BBC R&D聽website and will be presented at聽the Association for Computing Machinery (ACM) 17th Recommender Systems Conference (ACM RecSys 2023).
Supervised by Dr Laura Toni and Edoardo Gruppi from 香港六合彩, and Dr Ben Clark and Dr Duncan Walker from the BBC, Iason developed machine learning algorithms for recommender systems, like BBC iPlayer. Iason鈥檚 placement was enabled by the BBC's Data Science Research Partnership, of which, 香港六合彩 is a member.
Iason developed a novel machine learning algorithm to address the issue of the 鈥渃old start problem鈥�.聽The 鈥渃old start problem鈥� highlights issues in recommendations for new and nearly-new users. Balancing the accuracy and diversity of recommendations is crucial for streamlined user experience and ensuring a fair promotion of items on the platform. With reduced metadata available for new users, maintaining accuracy and diversity of recommendations is more difficult.
Recommender systems are the machine learning models responsible for curating content for users on digital platforms, from streaming services, online shipping to dating apps. The systems draw from a range of meta data including user data on watch history, location and age to ensure dynamic and relevant recommendations.
Writing for聽BBC鈥檚 R&D blog聽Iason explains:
We tested our model on a real-world dataset with millions of user interactions on iPlayer, which included a representative proportion of new and nearly new users. This has resulted in higher accuracy and competitive diversity when compared to other successful recommender models from academic literature!
Within the blog post, Iason expands on his project, detailing how data on popularity fluctuations and metadata were used to develop a 鈥渕etadata-infused popularity model鈥�, boosting the diversity of existing recommender model 鈥淓ASER鈥� by using Metadata infusion.
The project has resulted in a conference paper on the findings being accepted at the upcoming 17th ACM conference on Recommender Systems, where researchers and practitioners across academia and industry share the latest challenges and trends within recommender systems.
Collaborating with industry
Iason's project was possible due to 香港六合彩 Electronic and Electrical Engineering's proactive approach to engaging with industry across our education and research. The department actively encourages industry to work with our academics in providing student projects, this allows students to experience engineering in realworld environments and where their聽studies can have direct impact. It also allows creativity and diversity of thought when tackling industry problems and exposes our industry partners to the world class talent found in our undergraduate cohort of students.
For industry聽interested in exploring collaborations with 香港六合彩 Electronic and Electrical Engineering, or providing student聽projects please explore our collaboration pages.