Explain Collaborative Filtering Approaches
Overview
This question evaluates understanding of collaborative filtering approaches and related competencies such as neighborhood-based versus matrix factorization methods, differences for explicit versus implicit feedback, pointwise versus pairwise loss trade-offs, regularization, cold-start and popularity-bias mitigation, evaluation metrics, and scalability considerations. It is asked in the Machine Learning domain to assess both conceptual understanding and practical application for designing, evaluating (e.g., NDCG, MAP) and scaling recommender systems in technical interviews.
Collaborative Filtering for Recommendations: Approaches, Losses, Regularization, Cold Start, Bias, Evaluation, and Scale
Context
You are designing a recommender system for a very large catalog and user base. Explain collaborative filtering (CF) approaches and how to implement and evaluate them at scale.
Tasks
- Explain user-based and item-based collaborative filtering and compare them with matrix factorization.
- Discuss how these methods differ for explicit feedback (e.g., ratings) vs implicit feedback (e.g., clicks, views, purchases).
- Compare loss choices: pointwise vs pairwise ranking (and when to use each).
- Describe regularization strategies.
- Propose cold-start strategies for new users and new items.
- Discuss how to handle popularity bias.
- Outline offline and online evaluation (e.g., NDCG, MAP) and their pitfalls.
- Address scalability considerations for large catalogs and many users.
Solution
Login required