various similarity measures
item-item similarity (first pub by glinden et al)
predict a user's rating of an item as the weighted sum of other items' ratings by the user, where weights are item similarities:
score[usr,itm] = avg(sim(itm,itm2) * score[usr,itm2] for itm2 in itms)
user-user similarity
predict a user's rating of an item as the weighted avg of other users' ratings for the item, where weights are user similarities:
score[usr,itm] = avg(sim(usr,usr2) * score[usr2,itm] for usr2 in usrs)
evaluation metrics: RMSE is typical
forward selection: add features one/some at a time
backward elimination aka recursive elimination: eg this faster/sloppier version of naive backward (which is opposite of forward)
start with all features in model candidates (for removal) = all features for each iteration, remove from candidates any feature whose exclusion yields no acc drop remove (from model and candidates) feature in candidates with biggest acc drop
combination: add & remove features, eg forward while optionally removing a feature at each step
increase weight along most-correlated var by $\epsilon$
let vector r = y
let vector beta = 0
iterate:
find x[j] most correlated with r
let delta = epsilon * sign(r, x[j])
set beta[j] += delta
set r -= delta * x[j]
identical to LASSO for orthogonal predictors, and similar in general case