Mining model for hotel recommendations (Kaggle Challenge)

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2 likes•1,145 views

The presentation describes an approach we devised to hotel recommendation systems and what could be done to improve it. It also contains a few obstacles I faced while programming it.

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Choice of Model
 Supervised learning model
◦ GBM
◦ LambdaMART
 Ensemble techniques
 Gradient Boosting Regression trees are a set
of flexible, non-parametric methods which fit
most supervised learning models.
 LambdaMART is a learning to rank algorithm
based on Multiple Additive RegressionTree
(MART)

Why GBM ?!
 Already implemented in python
 Successful application for other
recommender systems
 Implicit mapping of feature interactions
 Good with heterogeneous datasets
 Choice between different loss functions
(allows comparisons)

Problems & Solutions
 Careful tuning
◦ Grid search : hyperparameter tuning
 Not good at extrapolation
◦ Some other function to extrapolate
 Not good with sparse datasets
◦ PCA would help

Our approach
 PANDAS to sample data/fill missing values
◦ HDF5 format
◦ Fast access with PyTables
 Define GBM and PCA
 Piped GBM and PCA together
 Split data into train & test, source &
target sets
 Run Grid Search to find best parameters
 Train the estimator with training data

Contd.
 Apply the prediction model on test set
 Use the loss function (absolute error) to
calculate error measure
 Plot the error for each data point and
display the absolute error

Obstacles
 Biggest one was limited memory –
expected to run for more time not cause
a memory error
 Tuning to the right parameters
 Optimal method to tackle missing values
 Elimination of outliers as basic IQR
method eliminated most of the data
points.
 Implementing loss function (Initially did it
wrong and got error of 96.7)

Results
 Sampled 500,000 values and filled the
missing values
 Mean absolute error averaging around 8.4
 Very high, but considering we used only
1% of available data , it is acceptable

How to improve?!
 Implement robust PCA. PCA is sensitive to
outliers/missing values
 Use 10-20% of data for sampling
 Set the params to increase variance for
closer predictions
 Implement on computer with higher
memory, computing power & not on virtual
box (Result in good hyper-parameter tuning)
 Work around numpy to handle large arrays
and avoidValue Errors.

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Mining model for hotel recommendations (Kaggle Challenge)

1. MINING MODEL

2. Choice of Model  Supervised learning model ◦ GBM ◦ LambdaMART  Ensemble techniques  Gradient Boosting Regression trees are a set of flexible, non-parametric methods which fit most supervised learning models.  LambdaMART is a learning to rank algorithm based on Multiple Additive RegressionTree (MART)

3. Why GBM ?!  Already implemented in python  Successful application for other recommender systems  Implicit mapping of feature interactions  Good with heterogeneous datasets  Choice between different loss functions (allows comparisons)

4. Problems & Solutions  Careful tuning ◦ Grid search : hyperparameter tuning  Not good at extrapolation ◦ Some other function to extrapolate  Not good with sparse datasets ◦ PCA would help

5. Our approach  PANDAS to sample data/fill missing values ◦ HDF5 format ◦ Fast access with PyTables  Define GBM and PCA  Piped GBM and PCA together  Split data into train & test, source & target sets  Run Grid Search to find best parameters  Train the estimator with training data

6. Contd.  Apply the prediction model on test set  Use the loss function (absolute error) to calculate error measure  Plot the error for each data point and display the absolute error

7. Obstacles  Biggest one was limited memory – expected to run for more time not cause a memory error  Tuning to the right parameters  Optimal method to tackle missing values  Elimination of outliers as basic IQR method eliminated most of the data points.  Implementing loss function (Initially did it wrong and got error of 96.7)

8. Results  Sampled 500,000 values and filled the missing values  Mean absolute error averaging around 8.4  Very high, but considering we used only 1% of available data , it is acceptable

9. Error graph

10. How to improve?!  Implement robust PCA. PCA is sensitive to outliers/missing values  Use 10-20% of data for sampling  Set the params to increase variance for closer predictions  Implement on computer with higher memory, computing power & not on virtual box (Result in good hyper-parameter tuning)  Work around numpy to handle large arrays and avoidValue Errors.

Mining model for hotel recommendations (Kaggle Challenge)

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