Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
•
1 j'aime•371 vues
For Roularta, a news & media publishing company, it is of a great importance to understand reader behavior and what content attract, engage and convert readers. At Roularta, we have built an AI-driven article quality scoring solution on using Spark for parallelized compute, Delta for efficient data lake use, BERT for NLP and MLflow for model management. The article quality score solution is an NLP-based ML model which gives for every article published – a calculated and forecasted article quality score based on 3 dimensions (conversion, traffic and engagement).
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
Similaire à Building an ML Tool to predict Article Quality Scores using Delta & MLFlow (20)
Plus de Databricks
Plus de Databricks (20)
Dernier
Dernier (20)
Building an ML Tool to predict Article Quality Scores using Delta & MLFlow
- 1. Building an ML tool to predict article quality scores using Delta & MLflow Ivana Pejeva Data Scientist @ element61
- 2. What’s the challenge For past articles, Roularta wants to… ▪ analyze which article did well in bringing traffic, engagement & conversions Quality scoring of an article Predicting quality of an article Roularta wants to know the aricle quality... ... but there is an infinite amount of possible KPIs one needs to track ...and the goal is to simplify/standardize/automate an objective measurement For new articles, Roularta wants to… ▪ predict which article will bring good conversions, traffic and engagement
- 3. What we offer editorial teams ▪ Calculates article scores on historical articles ▪ Predicts article scores on new articles CMS Content Behavior Reader behavior Structured streaming Structured streaming Azure Data Lake Gen2 Feature extraction Predictive Model Article Score Score calculation Data tool which
- 4. What do we want to predict How much traffic is an article bringing to the site? Is this article going to bring good conversions? Will this article keep people engaged? Traffic Score Conversion Score Engagement Score
- 5. The flow for calculating and predicting article scores Gather all pageviews and content data • Number of pageviews • Pageviews from Social media • Article author • Publishing date • … Calculate series of traffic, engagement and conversion scores Traffic Score: 0.7 Engagement Score: 0.2 Conversion Score: 0.5 Calculate quality stars for each article Traffic Score: Engagement Score: Conversion Score: Calculate quality score Calculate overall quality score based on traffic, engagement and conversion score Predict scores Predict the traffic, engagement and conversion scores on new articles
- 6. What’s the data ▪ Reading Behavior ▪ Coming from a CDP tracker ▪ Pageviews ▪ Content Behavior ▪ Coming from the CMS ▪ Details on the content CMS Content Management System
- 7. How we prepared the data ▪ Number of pageviews ▪ Time on page ▪ Social media shares ▪ Registrations ▪ Subscriptions ▪ Bounce rate ▪ … ▪ Article text ▪ Publication time ▪ Author ▪ Tags in article ▪ How long is the article ▪ Topic of article ▪ … Calculate article scores Predict article scores
- 8. Calculation of article scores is a computationally intensive job ▪ A lot of the metrics require looking at a specific window of data from millions of rows ▪ e.g. calculating the number of visitors to an article where the user was not seen on the site for the past 30 days ▪ This would require to always look at a specific window of data for each article ▪ Very expensive operation as we are looking over millions of rows ▪ Keep intermediate delta lake tables ▪ e.g. Keep the visitors from the last 30 days in an intermediate delta table ▪ Avoid using windowing function over a huge amount of data ▪ > 10x improve in performance
- 9. The role of Delta ▪ ~2 million pageviews per day we need to process in streaming mode ▪ ~ 250k number of articles ▪ 50 brands ▪ Delta in silver and gold layer for data ingestion ▪ Intermediate delta tables for extracted features ▪ Time travel to recreate ML experiments Delta to accelerate data ingestion and feature engineering pipelines
- 10. The feature extraction process CMS Content Behavior Reader behavior Calculate traffic, engagement and conversion scores Create features for each article Add labels to articles • Is it a short/long article? • Does the teaser have images? • Tags in article • Authors of article • When was it published? • Teaser text
- 11. NLP BERT language model ▪ BERTje – a dutch based BERT model ▪ Extract representations from article text ▪ Leverage pandasUDF for better performance ▪ Highly improved ML model performance For extracting features from articles text
- 12. How we build the predictive model CMS Content Behavior Reader behavior Calculate traffic, engagement and conversion scores Create features for each article Add labels to articles • Is it a short/long article? • Does the teaser have images? • Tags in article • Authors of article • When was it published? • Teaser text ML Pipeline for feature transformation • Binarize features • Encode string column to label indices • TF-IDF to reflects importance of words in tags, topics and collections of articles • Scale data with mean and std • Create feature vectors Train Model • Multi-class classification problem (predict the number of stars (1-5) • Random Forest Classifier • Cross Validation and log model metrics with • Multiclass Classification Evaluator Register model
- 13. The role of MLflow ▪ Train ML models per brand and per score ▪ We need to ▪ Track performance for all models ▪ Promote new model versions fast ▪ Serve models to score new data For model tracking, register and serve
- 14. Results Method: ▪ Predictions made on articles from one brand ▪ Total number of articles 2368 Results: ▪ In 93% of the cases a quality article (4 or 5 stars) is given 4 or 5 stars by the model ▪ A low-quality article (1 or 2 stars) is given 4 or 5 stars in only 2% of the cases Details: ▪ Most important features are Teaser text and topics
- 15. Summary ▪ Use Delta to accelerate data ingestion and feature extraction ▪ Use NLP BERT for text representations from articles ▪ Use Mlflow for tracking, registering and serving ML models
- 16. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.