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A chatbot that helps you order your beer and pay for it. Able to give recommendations, react to your emotions (sadness, anger), talk about the weather, tell jokes and more! |
Demos
Chatbot Evaluation Platform
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Questionnaire to evaluate the performance of four different chatbots. |
Chatbot
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We present a chatbot that generates responses to people’s queries. The model is trained using 35,429 pair of conversations from the Big Bang Theory. |
Visualization tool for tweets related to food and mood
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We present a tool that enables users to browse hundreds of tweets related to both food and mood. By placing these tweets on a wheel, the user can visualize how moods are correlated with a certain food. |
Visualization of food and mood
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We present a tool that allows people to interactively review their food journeys, and to explore the interesting correlations between their nutrition intake and emotions. |
Visualization tool for activities of daily living
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We present a tool to visualise physical exercise patterns of 48 people, who wore accelerometers that measured steps and calorie expenditure. |
Emotions in Tweets about the Olympics
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Temporal visualization of the emotions captured in the tweets about Olympic Games. |
interactive FishEye-View Interface
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We developed an interactive products recommender system based on a force-directed layout and fish-eye view. This system provides a search tool and a critiquing interface to query items intuitively. The link below gives you access to the desktop demo but there is also a mobile version of this recommender system. |
Group Fun
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We have developed a music group recommender system named GroupFun, which is a Facebook application that recommends music for groups of users for events, such as a graduation party. The functions of GroupFun mainly include: 1) group management, 2) music recommendation. Users are able to create a group, invite their friends to group, and join other groups. We are evaluating recommendation algorithms and user interfaces on this application. |
Survey on Hybrid Critiquing
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Given their respective strengths, a Hybrid of Example-Critiquing (EC) and Dynamic-Critiquing (DC) was developed to combine them together on the same screen, so that users can not only specify their own critiques if necessary with EC, but also obtain the knowledge of remaining recommendation opportunities and pick the suggested critiques by DC if one of them matches their desires. |