Comments (3)
Hi @risufaj!
Thank for your interest in the library.
Great question! Cold-start is a challenging problem in many recommender system problems. Note we have a warm_start
function in the MAB object that can be used to warm-start cold arms. In order to warm-start the multi-armed bandit we need a list of features for each arm that can be used to calculate pairwise similarities between the arms. See here for an example how it's used.
An obvious next question is where to get these features. In practice, one would typically have some textual information for arms, but not necessarily numerical features. Fortunately, we also developed a library called TextWiser that can be used for text featurization based on a rich set of methods. Check it out if it sounds interesting.
from mabwiser.
Hi @bkleyn,
Thank you for answering!
I understand that using the function you mentioned, it would be possible to warm start an arm that is most similar to some existing arms, for which you have data. In situations where there aren't any data recorded for any of the arms, I suppose the best could be to have some sort of data collection phase, right?
from mabwiser.
Yes, indeed! This is in fact an active research area, and we recently studied how to speed-up that data collection phase with publications at CPAIOR'21 and IJCAI'21.
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Related Issues (20)
- Using categorical variables in the context HOT 2
- init order HOT 2
- Predict and Predict_expectation difference in results HOT 4
- number of arms HOT 2
- Simulator usage - train and test split for target encoded features to avoid leakage HOT 1
- Thompson Sampling for Gaussian priors? HOT 6
- How to use Categorical variables as context? HOT 1
- Evluation erroring out HOT 5
- `context` isn't passed to `_parallel_fit` in Thompson Sampling HOT 3
- Cascading feedback type HOT 3
- Is there a way to retrieve DecisionTree output? HOT 4
- [Question] A way to only predict arms from a given subset? HOT 2
- Need an LP and NP Type Definition
- interpreting `predict_expectations` HOT 1
- Make protocols out of LearningPolicyType and NeighborhoodPolicyType? HOT 3
- Save the state of Contextual MAB HOT 3
- Consistently get the actual expected value for each arm HOT 3
- Parallel fit/predict for contextual policies HOT 1
- There's no good way of getting the rewards of arms, period. HOT 1
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