Mengqi Liao

schoolPennsylvania State Universitybar_chart#3953 Global Rank

Total Papers

HCI Rank

#3953

Research Areas

Years Active

Recent Publications

When Recommender Systems Snoop into Social Media, Users Trust them Less for Health Advice

Recommender systems (RS) have become increasingly vital for guiding health actions. While traditional systems filter content based on either demographics, personal history of activities, or preferences of other users, newer systems use social media information to personalize recommendations, based either on the users’…

CHI '221st AuthorRecommender System UX

User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering

Three of the most common approaches used in recommender systems are content-based fltering (matching users’ preferences with products’ characteristics), collaborative fltering (matching users with similar preferences), and demographic fltering (catering to users based on demographic characteristics). Do users’ intuiti…

CHI '211st AuthorAI-Assisted Decision-Making & Automation +2

How should AI Systems Talk to users when Collecting their Personal Information? Effects of Role Framing and Self-referencing on Human-AI Interaction

AI systems collect our personal information in order to provide personalized services, raising privacy concerns and making users leery. As a result, systems have begun emphasizing overt over covert collection of information by directly asking users. This poses an important question for ethical interaction design, whic…

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