Fairness among New Items in Cold Start Recommender Systems

  • Heater, DropoutNet, DeepMusic, and KNN
  • Investigated fairness among new items in cold start recommenders.
  • Identified prevalent unfairness in these systems.
  • Proposed a novel learnable post-processing framework to enhance fairness.
  • Developed two specific models, Scale and Gen, following the framework.
  • Conducted extensive experiments, showing effectiveness in enhancing fairness and preserving utility.
  • Future research planned to explore recommendation fairness between cold and warm items in a unified scenario.
  • This work examines the fairness among new items in cold start recommendation systems, highlighting the widespread presence of unfairness.
  • To address this issue, a novel learnable post-processing framework is introduced, with two specific models – Scale and Gen – designed following this approach.
  • Extensive experiments demonstrate the effectiveness of these models in enhancing fairness while maintaining recommendation utility.
  • Future research aims to explore fairness between cold and warm items in a unified recommendation context.
  • Mean Discounted Gain

Data drift

Data drift refers to the change in the statistical properties of the data that a model is processing over time. This can lead to decreased model performance if the model was trained on data with different statistical properties. Detecting data drift without access to labels can be more challenging, but it is still possible through various techniques.

  1. Statistical Tests: You can conduct statistical tests on the features in your data to check for changes in distribution. Kolmogorov-Smirnov or Chi-squared tests are often used to compare the distribution of the current data with the distribution of the data on which the model was trained. If the test indicates a significant difference, it could be a sign of data drift.

  2. Monitoring Feature Statistics: Continuously monitor summary statistics (e.g., mean, median, standard deviation, etc.) of your input features. If there are significant changes in these statistics over time, it may indicate data drift. You can set threshold levels to trigger alerts if the statistics deviate beyond acceptable bounds.

  3. Using Unsupervised Learning: Techniques like clustering or dimensionality reduction (e.g., PCA) can be used to represent the data in a way that makes it easier to spot changes. By regularly fitting these techniques to the incoming data and comparing the results with the original training data, you might identify shifts in the data structure.

  4. Comparing Prediction Distributions: Even without labels, you can compare the distribution of predictions made on the current data to the distribution of predictions made on the training or validation data. A significant shift might indicate a change in the underlying data distribution.

  5. Residual Analysis: If you can obtain a small subset of labeled data, you can analyze the residuals (the difference between the predictions and the true labels). A change in the distribution of residuals over time might be indicative of data drift.

  6. Creating a Proxy for Labels: If your production environment involves users interacting with the predictions (e.g., clicking on recommended items), you might create a proxy for true labels based on user behavior and use this to detect changes.

  7. Human-in-the-Loop: Depending on the application, it might be feasible to introduce a human review process to periodically evaluate a subset of the predictions. While not fully automated, this can be a powerful way to detect issues that automated methods might miss.

  8. Use of Drift Detection Libraries: There are libraries and tools designed specifically for drift detection, like the Python library Alibi-Detect, that can be implemented to monitor for data drift.

Remember, detecting data drift is not always straightforward, especially without access to true labels. The appropriate approach may depend on the specifics of your data, model, and application. It’s often useful to combine multiple methods to create a more robust detection system. Regularly reviewing and updating your model with new training data reflecting the current data distribution is an essential part of maintaining model performance.

Causal Ranker

Certainly! Here’s a bullet-point summary of the information you provided about the Causal Ranker Framework by Netflix:

  • Overview:
    • Authors: Jeong-Yoon Lee, Sudeep Das.
    • Purpose: To enhance recommendation systems by incorporating causal inference into machine learning.
    • Concept: Moving beyond mere correlations to understand causal mechanisms between actions and outcomes.
  • Machine Learning vs Causal Inference:
    • Machine Learning: Focuses on associative relationships, learning correlations between features and targets.
    • Causal Inference: Provides a robust framework that controls for confounders to estimate true incremental impacts. This adds understanding of the causal relationship between actions and results.
  • Application at Netflix:
    • Current Systems: Netflix uses recommendation models for personalizing content on user homepages.
    • Need: Netflix identified the potential benefit of adding algorithms that focus on making recommendations more useful in real-time, rather than merely predicting engagement.
  • Causal Ranker Framework:
    • Introduction: A new model applied as a causal adaptive layer on top of existing recommendation systems.
    • Components: Includes impression (treatment) to play (outcome) attribution, true negative label collection, causal estimation, offline evaluation, and model serving.
    • Goal: To find the exact titles members are looking to stream at any given moment, improving recommendations.
    • Reusability: Designed with generic and reusable components to allow adoption by various teams within Netflix, promoting universal improvement in recommendations.
  • Implications:
    • Scalability: By combining machine learning with causal inference, the framework offers a powerful tool that can be leveraged at scale.
    • Potential Impact: Enhancing personalization, meeting user needs more effectively, and aligning recommendations with users’ immediate preferences.
  • The Causal Ranker Framework symbolizes an innovative step in recommendation systems, emphasizing the importance of understanding causal relationships and catering to real-time user needs. Its flexibility and comprehensive design have positioned it as a potential game-changer within Netflix’s personalization efforts and possibly beyond.

Question bank

  • Behavior interview with hiring manager
    1. Past projects - most challenging part, your role
  • The most challenging thing about being a manager is also the most rewarding. As the team’s manager, I’m responsible for not just my own success but that of my team as well. In that sense, my charter typically involves a much bigger scope than as my prior role as an individual contributor. However, navigating a big ship comes with its own set of unique responsibilities. You are responsible not only for yourself, but for your team. So you must continually measure their performance, set clear expectations/goals/priorities, make sure the communication is crisp and clear, motivate them, and keep them focused. At the end of the day, it is a great feeling to be able to accomplish this.
  • Also, another important aspect of this position would be to build the relationship with my employees because that will take time. However, I also feel it is one of the most rewarding part of this position. I enjoy relationship-building and helping others to achieve their success.
  1. Tell me a time when you disagree with the team
    • I can tell you a time where I disagreed with my leadership.
    • At the time, we were working on content to content recommendations, books to podcast with cross collaborations with Amazon retail, audible and wondery (podcast platform).
    • There were a lot of novel insights and a unique architecture we approached to solve this and thus, we decided to get a publication out of this.
    • The process to start this off at Amazon, requires Director level approval to kick off the writing process, however, my managers manager, who sits under the Director, wanted to set up a meeting to discuss this before we presented it to the Director to approve.
    • This went against Amazon’s policies and would hinder time to submit to the conference. I respectfully,
  2. Tell me a time when you inherited a system in bad shape
  3. How do you prioritize
    • Name five devices you can watch Netflix on – Systems engineer candidate
    • What would you do if you were the CEO? – Partner product group candidate
    • Describe how you would deal with a very opinionated coworker.
      • I think netflix coins this term as “brilliant jerks.” Engin. Complaints about everyone on the team.
      • They were
  • Tell me about a previous time you screwed up at your previous job.
  • What has been the biggest challenge while you work?
  • How do you improve Netflix’s service? – Financial analyst candidate
  • Who do you think are Netflix’s competitors and why? – Creative coordinator candidate
  • How do you test the performance of your service? – Software engineer candidate

  • Because Netflix is focused on maintaining a strong company culture—the majority of questions that the hiring manager will ask will be situational, cultural, and behavioral-style questions. Like the example questions above.
  • When asked these questions it is very easy to get nervous and mix up all of our responses. In this situation, the best way to stay structured is by using the STAR Methodology, which stands for Situation, Task, Action, and Result
  • Let’s dive into an example so that you can better understand this method:
  • Example question:
  • How did you handle a task where you had a deadline that you couldn’t meet?
  • Situation:
  • Don’t generalize the information that you are conveying**. Be as specific as possible when describing the situation, so that the person asking the question understands the context.

  • Example: Because the last company I was working at was growing so quickly, we did not have enough staff to cover all of the projects. Most people like me had more projects than we could handle, and that did cause stress and tension.

  • Task:
  • Describe your responsibility and the goal you were working towards.

  • Example: I was a project manager that was in charge of application releases. I had to make sure that the applications were launched in the right order and on the right date.

  • Action:
  • You must provide what specific actions you took towards solving the problem. Also, make sure that you do not focus on talking about any other team member. Try using the word “I” and not “we”.

  • Example: To make sure that I wasn’t too overwhelmed, I created a project timeline. I then organized all of the app launches in order of priority. If an application was not going to be launched on time or if it had low priority—I made sure to bring this up to my superiors and explain what my plan was.

  • Result:
  • This is your time to shine. Describe the outcome of the situation in detail, and show how you were able to solve the problem.

  • Example: Because I created a timeline and took charge of prioritizing the launch, we were able to be much more efficient. Once the big launches were done, I was able to create much more time for the team. This led us to complete more projects than we thought was possible and generate more revenue for the company.

  • hm screening team lead, they asked about the current system in very, very detailed terms.
  • You must be very clear about your project and failure point. There are still a lot of bq, and the previous experience still has scenario based problems.
  • cross functional
  • Then I introduced myself. After talking about the background, I said that I want to go through all the projects on the resume for you? He said you tell me your favorite, so I will tell you one. After he finished speaking, he began to ask questions. If you want causal to be based on what assumptions, how did you rule out some possible reasons, are you confident that you ruled out other things that may affect causality? I said what I controlled, what fixed effects I added, so I was comparing with whom, and what robustness checks I did..
  • under what circumstance is the power the highest for A/B test
  • Suppose you want to do an experiment, that is, whether to use static pictures or dynamic videos on the netflix homepage, so that more people can sign up for subscription.
  • I said, first of all, I need to determine my population, ah, do you want to be global or just the United States. He said global.
  • Then I said that I want to determine my sample, it is best that a certain percentage of people from each country come in as a sample.
  • Then I need to determine my time. Then I have to take into account that the audiences who come in at different times in the morning, noon and evening are different. The audience who come in on weekdays and weekends are different. Holidays may also be a problem, but you can’t do this experiment. Years, so it must be at least a week? (Then my brother praised me! He said I thought well!)
  • Then I want to determine the outcome variable, that is whether to sign up.
  • Wow, a lot of details, I said just do a t test, if there is no problem with the randomization (for example, I check the balance)
  • What is the common misunderstanding of P value?
  • Ans: The hypothesis can only reject or not reject, but not accept.

your projects


  • A director, Uncle Bai, mainly asked BQ, what he thinks of their culture, and some general questions.
  • Surprisingly, I was even asked some questions about design, a/b test and Ml deployment, how to monitor data drift if the true label cannot be obtained in time on metaflow, etc.
  • It feels technically strong.
  • Kolmogorov


  • Since the goal is to prepare for the specific role at Netflix, focusing on applied aspects of econometrics and causal inference that relate to personalization, satisfaction estimation, and working with large-scale data, the study plan would be as follows:

Week 1-2: Introduction to Econometrics

  • Reading: “Introductory Econometrics: A Modern Approach” by Jeffrey M. Wooldridge - Focus on introductory chapters.
  • Online Course: Coursera’s “Econometrics: Methods and Applications” - Focus on the basic methods and applications.
  • Hands-on Practice: Work with simple datasets to apply linear regression and understand the assumptions behind it.

Week 3-4: Time-Series Analysis & Forecasting

  • Reading: “Applied Econometric Time Series” by Walter Enders.
  • Online Tutorial: “Time Series Analysis in Python” on DataCamp or similar platforms.
  • Project: Forecasting a time series data like stock prices or user activity trends.

Week 5-6: Causal Inference - Basics

  • Reading: “Causal Inference in Statistics: A Primer” by Judea Pearl.
  • Online Course: “Causal Inference” on Coursera by Columbia University.
  • Hands-on Practice: Implementing propensity score matching and other techniques on observational data.

Week 7-8: Experimental Design & A/B Testing

  • Reading: “Field Experiments: Design, Analysis, and Interpretation” by Alan S. Gerber and Donald P. Green.
  • Online Tutorial: A/B Testing tutorials on platforms like Udacity.
  • Project: Design a hypothetical A/B test for a feature that could enhance user satisfaction.

Week 9-10: Advanced Causal Inference & Machine Learning Integration

  • Reading: “Causal Inference for Statistics, Social, and Biomedical Sciences” by Guido W. Imbens and Donald B. Rubin.
  • Online Course: “Causal Machine Learning” on Coursera by University of Pennsylvania.
  • Hands-on Practice: Apply causal machine learning techniques to a complex dataset.

Week 11-12: Reinforcement Learning

  • Reading: “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto.
  • Online Course: “Reinforcement Learning Specialization” on Coursera by the University of Alberta.
  • Project: Build a simple recommendation system using reinforcement learning.

Week 13-14: Application to Real-World Problems

  • Case Studies: Research and analyze Netflix’s research papers or blogs related to personalization, satisfaction estimation.
  • Project: Work on a complex project that integrates econometrics, causal inference, and machine learning to solve a real-world problem similar to what Netflix is facing.

Ongoing: Networking & Keeping Up-to-Date

  • Conferences & Workshops: Attend industry conferences related to data science, econometrics, and machine learning.
  • Blogs & Podcasts: Follow related blogs and podcasts like “Not So Standard Deviations” to keep up with the latest in the field.

Remember, this study plan can be tailored to fit your specific needs and existing knowledge base. It combines a mix of theoretical understanding with hands-on practice and real-world applications, focusing on areas most relevant to the Netflix role.