At any rate, if CSHW can be used to build a good quantitative model of human-human interactions, it might also be possible to replicate these dynamics in human-computer interactions. This could take a weak form, such as building computer systems with a similar-enough interactional syntax to humans that some people could reach entrainment with it; affective computing done right.
[[Aligning Recommender Systems]]
It has been demonstrated that this formulation is almost equivalent to a SLIM model,[9] which is an item-item model based recommender
So a pre-trained item model can be used to make such recommendations.
The user's latent factors represent the preference of that user for the corresponding item's latent factors
The higher the value of the dot product between the two, the higher the preference.
two lower dimensional matrices
Not necessary (in fact, often not) square. Typically each user is represented by a vector of dimension strictly less than the number of items and vice versa.
a study by Stephen Schueller, published last year in the Journal of Positive Psychology, found that people assigned to a happiness activity similar to one for which they previously expressed a preference showed significantly greater increases in happiness than people assigned to an activity not based on a prior preference. This, writes Schueller, is “a model for positive psychology exercises similar to Netflix for movies or Amazon for books and other products.”