AI News, Predicting CTR with online machine learning

Predicting CTR with online machine learning

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Identifying (and serving) those ads that have a higher probability of a click, translates into more profit and a higher quality: Behavorial retargeting is a form of online advertising where the advertisements are targeted according  to previous user behavior, often in the case where a visit did not result in a sale or conversion.

The engine is capable of predicting effective personalized advertisements on a web scale: real-time optimization of ad campaigns, click-through rates and conversion.

behavioral numerical feature may be the count of previous purchases. A behavioral categorical feature may be the product ID that was added to a shopping cart, but not purchased.

The engineering blog also shows how Criteo is creating web graphs (without using web crawlers) to engineer new features.

The web as seen by Criteo [from the Criteo Engineering blog] For this contest Criteo’s R&D division, CriteoLabs, has released a week’s worth of click data.

We have 13 columns of integer features (mostly count features), and 26 columns with hashed categorical features.

Though the exact nature of the features is unknown to us, according to a competition admin (Olivier Chapelle), they fall in the following categories: Our task now is to create a model that will predict the probability of a click.

think that sticking with your favorite machine learning tool or algorithm for all classification and regression problems, is like picking a chess opening and only playing that against all opponents.

It won’t perform the best on all competitions, though it will perform in most (Multiclass Classification, Regression, online LDA, Matrix Factorization, Structured Prediction, Neural network reduction, Feature interactions), and is a robust addition to many ensembles.

The collected click data is huge (often far larger than fits into memory) or unbounded (you may constantly collect new categorical feature values).

That the competition metric really is logaritmic loss means we gain a massive amount of information even by training a model with Vowpal Wabbit: With its holdout functionality one-in-ten samples will be used to calculate and report on the average loss.

(Edit: Thanks to Anuj for spotting that I forgot to specify the model when testing, code above updated.) After running the above command we should have our predictions in a text file (~100MB).

The process for creating baseline benchmark was: We beat the logistic regression benchmark with our first submission. Vowpal Wabbit truly is an industry-ready tool for machine learning on large and high dimensional datasets.

Online Learning Guide with Text Classification using Vowpal Wabbit (VW)

A large number of E-Commerce and tech companies rely on real time training and predictions for their products.

This is used as an input to their auction mechanism, apart from a bid from the advertiser to decide which ads to show to the user.

Stackoverflow uses real time predictions to automatically tag a question with the correct programming language so that they reach the right asker.

An election management team might want to predict real time sentiment using Twitter to assess the impact of their campaign.

In the second section, we’ll look at an example of text classification using an online learning framework called Vowpal Wabbit (VW).

One way is to download a large corpus of emails, train a model on it and subsequently test it on unseen examples.

Going back to our example of spam classification, imagine a situation where the spammers have found a work-around and started bypassing the existing spam classifier.

These are nice implementations of SGD/online learning but we’ll focus on Vowpal Wabbit as it’s superior to sklearn’s SGD models in many aspects, including computational performance.

Also, real data can be volatile and we cannot guarantee that new values of categorical features will not be added at some point.

Once we fix the number of dimensions, we will need a hash function that will take a string and return a number between 0 and n-1 (in our case between 0 and 4).

I’ll compute the results for each word in our text: h(the) mod 5 = 0 h(great) mod 5 = 1 h(blue) mod 5 = 1 h(whale) mod 5 = 3 Once we have this, we can simply construct our vector as: (1,2,0,1,0) Notice that we just add 1 to the nth dimension of the vector each time our hash function returns that dimension for a word in the text.

If you feel hashing is really hurting the performance of your model, you could increase the number of bits required to produce the hash.

Consider a setting where a movie production company wants to build a real time IMDB review extraction and prediction system.

Help related to all the functions can be seen using the following command: The review dataset is provided in the form of text files at the provided link.

The following piece of python code helps to read all the text files and combine it into a single file.

Here, it is difficult to put the words (features) in different namespaces and hence we will just consider all the words separately without considering the interaction features.

This phrase has a definite negative sentiment, but if we don’t use n-grams, a positive weight will be learnt owing to the presence of the word ‘best’.

‘abominably, being a strictly negative word, produces mostly negative weights except in a few cases where the other word is highly positive, like ‘best’.

Train Vowpal Wabbit Version 7-10 Model

Trains a model using version 7-10 of the Vowpal Wabbit machine learning system

Category: Text Analytics This article describes how to use the Train Vowpal Wabbit Version 7-10 module in Azure Machine Learning Studio, to create a machine learning model using an instance of Vowpal Wabbit (version 7-10).

To incrementally train an existing model on new data, connect a saved model to the Pre-trained model input, and add the new data to the other input.

The primary users of Vowpal Wabbit in Azure Machine Learning are data scientists who have previously used the framework for machine learning tasks such as classification, regression, topic modeling or matrix factorization.

The Azure wrapper for Vowpal Wabbit has very similar performance characteristics to the on-premise version, which means that users can continue to build models, retrain, and score using the powerful features and native performance of Vowpal Wabbit, while gaining the ability to easily publish the trained model as an operationalized service.

Then, you can either upload the SVMLight format file to Azure blob storage and use it as the input, or you can modify the file slightly to conform to the Vowpal Wabbit input file requirements.

There are two ways to get an existing model for retraining: For examples of how Vowpal Wabbit can be used in machine learning, see the Azure AI Gallery: Also, see these resources: This section contains implementation details, tips, and answers to frequently asked questions.

The training data is downloaded in blocks from Azure, utilizing the high bandwidth between the store and the worker roles executing the computations, and is streamed to the VW learners.

Because the goal of the service is to support experienced users of Vowpal Wabbit, input data must be prepared ahead of time using the Vowpal Wabbit native text format, rather than the dataset format used by other modules.

Predicting CTR with online machine learning

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard. FastML

Now is your chance to play around with online learning, the hash trick, adaptive learning and logistic loss and get a score of ~0.46902 on the public leaderboard.

The competition of optimizing online advertisements with machine learning is like strawberries with chocolate and vanilla: You have large amounts of data, an almost endless variety of features to engineer, and profitable patterns waiting to be discovered.

Identifying (and serving) those ads that have a higher probability of a click, translates into more profit and a higher quality: Behavorial retargeting is a form of online advertising where the advertisements are targeted according  to previous user behavior, often in the case where a visit did not result in a sale or conversion.

The engine is capable of predicting effective personalized advertisements on a web scale: real-time optimization of ad campaigns, click-through rates and conversion.

behavioral numerical feature may be the count of previous purchases. A behavioral categorical feature may be the product ID that was added to a shopping cart, but not purchased.

The engineering blog also shows how Criteo is creating web graphs (without using web crawlers) to engineer new features.

The web as seen by Criteo [from the Criteo Engineering blog] For this contest Criteo’s R&D division, CriteoLabs, has released a week’s worth of click data.

We have 13 columns of integer features (mostly count features), and 26 columns with hashed categorical features.

Though the exact nature of the features is unknown to us, according to a competition admin (Olivier Chapelle), they fall in the following categories: Our task now is to create a model that will predict the probability of a click.

think that sticking with your favorite machine learning tool or algorithm for all classification and regression problems, is like picking a chess opening and only playing that against all opponents.

It won’t perform the best on all competitions, though it will perform in most (Multiclass Classification, Regression, online LDA, Matrix Factorization, Structured Prediction, Neural network reduction, Feature interactions), and is a robust addition to many ensembles.

The collected click data is huge (often far larger than fits into memory) or unbounded (you may constantly collect new categorical feature values).

That the competition metric really is logaritmic loss means we gain a massive amount of information even by training a model with Vowpal Wabbit: With its holdout functionality one-in-ten samples will be used to calculate and report on the average loss.

(Edit: Thanks to Anuj for spotting that I forgot to specify the model when testing, code above updated.) After running the above command we should have our predictions in a text file (~100MB).

The process for creating baseline benchmark was: We beat the logistic regression benchmark with our first submission. Vowpal Wabbit truly is an industry-ready tool for machine learning on large and high dimensional datasets.

Lessons learned from the Hunt for Prohibited Content on Kaggle

Previously we looked at detecting counterfeit webshops and feature engineering.

Averaging the outputs from two moderately inspired Vowpal Wabbit models gets one comfortably in the top 10% range and near the top 10 leaderboard.

First line from Vowpal Wabbit’s test set Using this data agnostic approach and very little to no feature engineering, one can use Vowpal Wabbit to get good scores.

If you have a good moderator labeled dataset, but no good solution yet, contact me or leave a message: our team would love to keep working on such datasets.

I’ve worked with European languages, which do have their fair share of diacritics and other arcane symbols, but Windows + The Python Benchmark + Russian text equalled zero for me.

By (incorrectly) answering a question by yr on the forums, I finally found out that the dataset when read on Windows produced around 1.5 million lines, and when read with Pandas or on other platforms would give the full size.

realize that in Kaggle competitions one may be disrespectful of the context (domain knowledge) of the data to a degree, but one should always respect the syntax.

I can not remove this prior belief that Machine Learning can combat online illicit and scam content, so I am afraid I will fall prey to a subtle form of overfit.

To do this correctly I would need a way to realistically reproduce a new test set, but one that is created one week after I created my model, preferably by real-life users of the model.

If your site creates a lot of data and faces a similar problem of spam and illicit content, contact me or leave a message, I’d love to chat with you.

The intro image is from a commercial from Avito.ru and the photo of president Carter refusing refuge to a Vowpal Wabbit was given to me by a man in a trench-coat inside a poorly lit parking lot.

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