Collaborative Filtering
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What is Collaborative Filtering?
Collaborative filtering is a technique used to make automatic predictions about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption is that if users have similar preferences in the past, they will have similar preferences in the future. It is commonly used in recommender systems to suggest items (e.g., movies, music, products) that a user might like, based on the preferences of other users with similar tastes.
What other technologies are related to Collaborative Filtering?
Collaborative Filtering Competitor Technologies
Gradient Boosting Trees
No summary available
Can be used as an alternative approach to collaborative filtering by directly predicting user-item interactions based on user and item features rather than relying on user-item matrix factorization.
Wide and Deep
No summary available
A hybrid approach that combines linear models (wide) and deep neural networks (deep) to balance memorization and generalization, offering a more comprehensive recommendation approach.
Deep Neural Networks
Can be used to learn complex user-item interaction patterns without explicit matrix factorization, offering an alternative approach to traditional collaborative filtering.
Logistic Regression
A basic model that can be used to predict user-item interactions based on user and item features, acting as a simpler alternative to collaborative filtering.
Random Forest
An ensemble learning method that can be used to predict user-item interactions based on user and item features, serving as an alternative to collaborative filtering.
decision trees
Can be used to predict user-item interactions based on user and item features, serving as an alternative to collaborative filtering.
neural networks
Can be used to learn complex user-item interaction patterns without explicit matrix factorization, offering an alternative approach to traditional collaborative filtering.
reinforcement learning
Can be used to dynamically optimize recommendations based on user feedback and long-term rewards, providing an alternative method to collaborative filtering which typically focuses on predicting explicit ratings.
Number of organizations that mention technology
155
Collaborative Filtering
21
Gradient Boosting Trees
6
Wide and Deep
447
Deep Neural Networks
1,081
Logistic Regression
1,677
Random Forest
1,136
decision trees
2,931
neural networks
2,379
reinforcement learning
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Collaborative Filtering Complementary Technologies
Matrix Factorization
A key technique used in collaborative filtering to reduce dimensionality and predict user preferences by decomposing the user-item interaction matrix.
Factorization Machines
No summary available
A generalization of linear models and matrix factorization. It can be used to incorporate side information and model higher-order interactions, enhancing collaborative filtering.
Word2Vec
Can be used to generate embeddings for items or users based on interaction sequences, thus providing features to augment collaborative filtering.
Number of organizations that mention technology
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Which job functions commonly mention Collaborative Filtering?
Machine Learning
0.2% of all Machine Learning jobs mention Collaborative Filtering
View 173
jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention Collaborative Filtering
View 181
jobs on Sumble
Applied Scientist
5 Applied Scientist jobs mention Collaborative Filtering
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jobs on Sumble
Data, Analytics & Machine Learning
388 Data, Analytics & Machine Learning jobs mention Collaborative Filtering
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jobs on Sumble
Data Analyst
28 Data Analyst jobs mention Collaborative Filtering
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jobs on Sumble
Which organizations are mentioning Collaborative Filtering?
Walmart
Retail Trade
Amazon
Retail Trade
Mastercard
Finance and Insurance
PetSmart
Retail Trade
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