CODEWITHSUNDEEP
algorithm
Anaid Villamizar
Anaid Villamizar

Reputation: 73

Contextual-Bandit Approach: Algorithm 1 LinUCB with disjoint linear models

I am trying to implement the algorithm called LinUCB with disjoint linear models from this paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" http://rob.schapire.net/papers/www10.pdf

This is the algorithm: Algorithm 1 LinUCB with disjoint linear models

I am confused about the features vector Xt,a (I highlighted on the algorithm). Is the feature vector related to information (context) of the article(arm) or the user?

I would appreciate your help. Thank you

Upvotes: 0

Views: 971

Answers (2)

w.g.l.
w.g.l.

Reputation: 3

In the most general case, the feature vector X_(t,a) is allowed to be a function of both the user context c_t and the arm a, or X_(t, a) = phi(c_t, a). Note that this could simply be a subset: each arm might have different features it uses to predict an outcome, or in other words X_(t, a) is a subset of c_t.

For example, if a movie recommendation website is deciding which movie to recommend, they might need different information from the user when attempting to predict if he'll like sci-fi movies versus drama movies. This different information is reflected in the fact that features are allowed to vary by arm.

Alternatively, it might be the case that X_(t, a) is the same for all a, i.e. X_(t, a) = X_t. For example, when trying to learn the best medical dosage, the algorithm might want to know height, weight, and age for all the patients. In this case the features would not vary by arm.

Upvotes: 0

jspcal
jspcal

Reputation: 51904

The feature vector x_t,a applies to both the user and the arm.

The vector xt,a summarizes information of both the user ut and arm a, and will be referred to as the context.

Upvotes: 1

Related Questions