Neal Lathia presented on using recommender systems for health applications. His research focuses on developing personalized health recommendations without diagnosing users by analyzing user ratings, tags, and text in health apps. He is also researching using sensed context like location and activity to recommend mood-improving activities. Additionally, his work includes developing a public transport recommender system that provides aggregated recent ratings to users.

1. demo: “using ratings to profile your health”
@neal_lathia
university of cambridge

2. collect #recsys recommend
preferences

4. user profiles for health
(gastrointestinal app)
“review” interaction: ratings, tags,
text
recommendations as tailored
health information
personalised vs. global health fact

7. Research Questions
● How can recommender systems be used in
(everyday) health domains?
● Prof ling, tracking, being “normal”
i
● How can recommender systems give tailored
information without diagnosing?
● Ethics? False positives? False negatives?

11. code app
https://github.com/nlathia/PooReview.Android

12. mood-based mobile
(recommender)
sensing + experience sampling
preferences:
linking behaviour ~ moods
just finished: data collection trial
with ~40 participants

13. (co)
location
movement phone
social
sensing
implicit
mic
proximity
experience
sampling
explicit

15. Research Questions
● How can we recommend activities based on
your mood and sensed context?
● How do sensor patterns ~ moods?
● How can this be measured?

16. public transport ratings
(recommender)
“review” interaction: ratings, text
“recommendations” as aggregated
recent ratings

18. Research Questions
● How can the system be bootstrapped?
● Cold-start: f lter bots
i social media
● How can the system solicit/incentivise
contributing ratings?
● How can we measure the quality and effect of
this information?

19. www.tubestar.co.uk android app