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Item-item collaborative filtering

Item-item collaborative filtering, or item-based, or item-to-item, is a form of collaborative filtering based on the similarity between items… Expand
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Related topics

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Collaborative filteringMovieLensOverfittingRecommender system
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
A Genre-Based Item-Item Collaborative Filtering: Facing the Cold-Start Problem
Recommender System is a technique which is used to recommend an item or product to a user based on the user's preference… Expand
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2019
2019
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems
This paper focuses on recommender systems based on item-item collaborative filtering (CF). Although research on item-based… Expand
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2018
2018
Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering
We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing… Expand
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Review
2018
Review
2018
BEP TI3806
As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller… Expand
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2018
2018
Eigenvalue analogy for confidence estimation in item-based recommender systems
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current… Expand
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2016
2016
Collaborative Filtering with Low Regret
There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we… Expand
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2015
2015
Regret Guarantees for Item-Item Collaborative Filtering
There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we… Expand
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2015
2015
Group Recommender Systems: Combining User-User and Item-Item Collaborative Filtering Techniques
Recommender Systems, these days, are no longer personal recommender systems, rather they are group recommender systems which list… Expand
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Highly Cited
2015
Highly Cited
2015
Letting Users Choose Recommender Algorithms: An Experimental Study
Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a… Expand
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2013
2013
Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation
tem-item collaborative filtering was widely used in item recommender system because of good recommend effects. However when… Expand
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