In a previous post, I mentioned Machine Learning acquiring skill with experience accumulated/computed from data.
data -> ML -> skill
Here are a few papers that try to apply Machine Learning to our life.
Abu-Mostafa, 2012
data: sales figures + client surveys
skill: give good fashion recommendations to clients
Tsanas and Xifara, 2012
data: characteristics of buildings and their energy load
skill: predict energy load of other buildings closely
Stalkamp et al., 2012
data: some traffic sign images and meanings
skill: recognize traffic signs accurately
Sadilek et al., 2013
data: Twitter data (words + location)
skill: tell food poisoning likeliness of restaurant properly
Recommender System
One hot application of ML is recommender systems. Netflix and Yahoo have held competitions related to recommender systems.
A competition held by Netflix in 2006.
100,480,507 ratings that 480,189 users gave to 17,770 movies.
A competition held by Yahoo in 2011.
252,800,275 ratings that 1,000,990 users gave to 624,961 songs.
How can machines learn our preferences?
A possible ML Solution
Learning:
known rating
→ learned factors
→ unknown rating prediction
I hope that this information will help. If you need any further information, please feel free to contact me. https://discord.gg/qHj8sHrctS
