The Fifth Elephant 2023 Winter edition - themes
- Talks from the healthcare sector
- Cutting edge developments with hardware and AI
- AI and risk mitigation
Attendance for The Fifth Elephant members only
The December edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend in-person conference.
Who will benefit from participating in The Fifth Elephant community:
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
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The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
dk
dhruvil karani
@althypothesis
Solving bias in recommender systems using negative sampling
Submitted May 12, 2023
Problem
Recommender systems suffer a major deficiency in their feedback loops.
When a user interacts with only a few out of many items on a website, we can only assume their interest in those specific items. Hence, the feedback is biased.
Regrettably, we don’t possess data on the items the user didn’t engage with, including those that weren’t presented. Our data only pertain to the positive class, and no explicit records of the negative class regarding binary classification exist. As a result, the model performs well for a small portion of items but not for the majority.
In addition, classical algorithms like matrix factorization do not directly support cold-start settings.
Implication
Every marketplace/social-media platform having millions of items/content pieces experience such a skew. Rarely a model can ensure consistent quality training for all items. In no time, this skew induces into the recommender system and hampers its performance.
Solution
The above two problems can be solved by producing negative examples using negative sampling.
Outline
In this talk, I wish to answer the following.
- Defining the problem with biased feedback. What happens if it is not carefully handled?
- How practical is this problem? What could be the potential business impact?
- What is negative sampling? Why should it work?
- What happens under the hood (accompanied by an example case study)?
- How to get the most out of this technique (tuning ideas)?
- How to measure the impact of negative sampling?
- Getting creative (Implementation from YouTube, Play Store, Meta, Airbnb)
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The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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