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Collaborative Filtering

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May 1, 2024 Updated May 10, 2025 22 minute read

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Collaborative filtering is a technique used by recommender systems to make automatic predictions about a user's interests by collecting preferences or taste information from many users (collaborating). The underlying assumption is that if person A has the same opinion as person B on an issue, A is more likely to have B's opinion on a different issue than to have the opinion of a randomly chosen person. This method powers many of the personalized experiences we encounter daily online, from product suggestions on e-commerce sites to movie recommendations on streaming services.

Path to Collaborative Filtering

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We've curated 20 courses to help you on your path to Collaborative Filtering. Use these to develop your skills, build background knowledge, and put what you learn to practice.

Sorted from most relevant to least relevant:

Nearest Neighbor Collaborative Filtering

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Basic Recommender Systems

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Project on Recommendation Engine - Advanced Book Recommender

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Fundamentos de sistemas recomendadores

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Recommender Systems

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Cluster Analysis, Association Mining, and Model Evaluation

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Building Recommender Systems with Machine Learning and AI

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Recommendation Systems with TensorFlow on GCP

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Recommender Systems and Deep Learning in Python

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Machine Learning Capstone

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Learn By Example: Hadoop, MapReduce for Big Data problems

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Mining Data from Networks

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Collaborative Filtering.

Collaborative Filtering Recommender Systems

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Focuses on the application of collaborative filtering algorithms and techniques for personalization. It is written by researchers who have extensive experience in this area and valuable resource for anyone interested in using collaborative filtering for personalization.

Collaborative Filtering Recommender Systems...

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Recommender Systems

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Focuses on the challenges of building recommender systems for large-scale data. It provides an overview of the state-of-the-art research on this topic and discusses various approaches for addressing these challenges.

Collaborative Filtering

Path to Collaborative Filtering

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Reading list