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Recommender system

A recommender system, also called a recommendation algorithm, recommendation engine, recommendation platform, or in the context of social media, simply algorithm is a type of information filtering system that suggests items most relevant to a particular user. The value of these systems becomes particularly evident in scenarios where users must select…

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The Netflix Prize

Evaluation

Artificial intelligence applications in recommendation

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Recommender system

Nodes111
Edges110
Triples218
Avg. degree1.98
Density0.018018
Components1

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Recommender system

Top relations

related to Further reading · 49
Recommender system → AAAI-2002, Alexander Felfernig, Algorithms, An Introduction, Archived, Artificial Intelligence, August, Bell, Bhasker, Canada, Chicago Press, Chris Volinsky, Computing Taste, Content-Boosted Collaborative Filtering, CUP, Dietmar, E-Commerce, Edmonton, Eighteenth National Conference, Gerhard Friedrich
related to Reproducibility · 25
Recommender system → Adomavicius, As, Bellogín, By, Deep, Ekstrand, Hence, IJCAI, In, KDD, Konstan, Machine Learning, More, Moreover, Recommender, Recommender Systems, RecSys, RecSys Challenge, Said, SIGIR
related to history · 16
Recommender system → ACM Software Systems Award, Another, Bellcore, Columbia University, Depending, Elaine Rich, GroupLens, Grundy, Her, Jussi Karlgren, MIT, Pattie Maes, Paul Resnick, She, SICS, Will Hill
related to The Netflix Prize · 12
Recommender system → As, Bell, BellKor's Pragmatic Chaos, From, Netflix, Netflix Prize, On, One, September, The, This, US
related to Collaborative filtering · 10
Recommender system → After, At, By, Collaborative, For, Left, One, Right, The, These
related to Alternative implementations · 9
Recommender system → Gonzalez, Google Supreme Court, In, LensKit, Of, RecBole, ReChorus, Recommender, RecPack
related to Example · 8
Recommender system → As, Last, Music Genome Project, Pandora, Pandora Radio, The, This, User
related to Performance measures · 8
Recommender system → A/B, DCG, Diversity, Evaluation, However, Netflix Prize, The, To
related to Session-based recommender systems · 7
Recommender system → Amazon, Domains, Most, Session-based, Techniques, These, YouTube
related to Content-based filtering · 6
Recommender system → Another, Content-based, In, It, These, This

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Important terminology

recommender systems user recommendation system recommendations users items filtering content collaborative data approaches item used information content-based learning based methods

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
knowledge-based systemsinstance ofas well as other systems0.80text
movies without requiring aninstance ofthey generate recommendations using this neighborhood.The collaborative filtering approach does not rely on machine analyzable content and therefore it is capable of accurately…0.80text
Bayesian Classifiersinstance ofSimple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques0.80text
cluster analysisinstance ofSimple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques0.80text
decision treesinstance ofSimple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques0.80text
and artificial neural networks in order to estimate the probability that the user is going to like the item.A key issue with content-based filtering is whether the system can learn user preferences from users' actions regarding one content sourceinstance ofSimple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques0.80text
use them across other content typesinstance ofSimple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques0.80text
cold startinstance ofThese methods can also be used to overcome some of the common problems in recommender systems0.80text
the sparsity probleminstance ofThese methods can also be used to overcome some of the common problems in recommender systems0.80text
as well as the knowledge engineering bottleneck in knowledge-based approaches.Netflix uses a hybrid recommender systems to make recommendations by comparing the watchinginstance ofThese methods can also be used to overcome some of the common problems in recommender systems0.80text
searching habits of similar usersinstance ofThese methods can also be used to overcome some of the common problems in recommender systems0.80text
recurrent neural networksinstance ofTechniques for session-based recommendations are mainly based on generative sequential models0.80text

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