NewsRecommendation contex vectors generator

It seems that NewsRecommendation generates the same context vector for each arm.
Reading LinUCB(2011 Li et al.) paper I have the impression that the context vector should be different (at least at some extend depending the arms features) for each arm. I am mistaken? Or the the implementation is that just for convenience?

ts_tutorial/src/news_recommendation/env_recommendation.py

Lines 39 to 44 in 3e229f7

    
           context = [] 
        
           context_vector = np.random.binomial(1,max(0,1/(self.dim-1)),self.dim) 
        
           context_vector[0] = 1         
        
           for i in range(self.num_articles): 
        
             context.append(context_vector) 
        
             self.current_rewards[i] = 1/(1+np.exp(-self.thetas[i].dot(context_vector)))

Thanks for raising this!

Yes I think you are right, and this looks incorrect to me.
I am now checking if there is any disconnect between this public repo and the code we ran for the experiments.

Any update on the issue?

Abbas Kazerouni has been managing this section of the code/experiment and we've discussed this.
He's having a look at this now - apologies for the delay.

Hello @DemirTonchev. Thanks for raising the question and apologies for delayed response.

The variable 'context_vector' in the code here represents only the user's context vector (z_t in 7.1 of the TS tutorial). As such, it will be generated once a user arrives and the rewards associated to different arms (i.e., articles) is computed for the same user with his/her contexts being fixed.

In our formulation, the characteristics of different arms are captured by unknown \theta_x vectors which are different across x's.

@abbas-kazerouni thanks for clarifying that. I though it was the z_{t, x} vector in TS tutorial. Do you have any code/simulation examples with the later type of characterizing the contextual vector?
Thanks for the TS tutorial paper :)

In the paper and the codes, we have considered a version of the problem where the context z_t only changes over time and is the same for all arms. Although, generalizing the algorithms to the more complex setting of Li et al. (2010) should be straightforward.

	context = []
	context_vector = np.random.binomial(1,max(0,1/(self.dim-1)),self.dim)
	context_vector[0] = 1
	for i in range(self.num_articles):
	context.append(context_vector)
	self.current_rewards[i] = 1/(1+np.exp(-self.thetas[i].dot(context_vector)))