A survey of collaborative filtering techniques
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From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.Abstract:
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.read more
Citations
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Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
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Link prediction in complex networks: A survey
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Deep Neural Networks for YouTube Recommendations
TL;DR: This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.
Journal ArticleDOI
TRY - a global database of plant traits
Jens Kattge,Sandra Díaz,Sandra Lavorel,Iain Colin Prentice,Paul Leadley,Gerhard Bönisch,Eric Garnier,Mark Westoby,Peter B. Reich,Peter B. Reich,Ian J. Wright,Johannes H. C. Cornelissen,Cyrille Violle,Sandy P. Harrison,P.M. van Bodegom,Markus Reichstein,Brian J. Enquist,Nadejda A. Soudzilovskaia,David D. Ackerly,Madhur Anand,Owen K. Atkin,Michael Bahn,Timothy R. Baker,Dennis D. Baldocchi,Renée M. Bekker,Carolina C. Blanco,Benjamin Blonder,William J. Bond,Ross A. Bradstock,Daniel E. Bunker,Fernando Casanoves,Jeannine Cavender-Bares,Jeffrey Q. Chambers,F. S. Chapin,Jérôme Chave,David A. Coomes,William K. Cornwell,Joseph M. Craine,B. H. Dobrin,Leandro da Silva Duarte,Walter Durka,James J. Elser,Gerd Esser,Marc Estiarte,William F. Fagan,Jingyun Fang,Fernando Fernández-Méndez,Alessandra Fidelis,Bryan Finegan,Olivier Flores,H. Ford,Dorothea Frank,Grégoire T. Freschet,Nikolaos M. Fyllas,Rachael V. Gallagher,Walton A. Green,Alvaro G. Gutiérrez,Thomas Hickler,Steven I. Higgins,John G. Hodgson,Adel Jalili,Steven Jansen,Carlos Alfredo Joly,Andrew J. Kerkhoff,Don Kirkup,Kaoru Kitajima,Michael Kleyer,Stefan Klotz,Johannes M. H. Knops,Koen Kramer,Ingolf Kühn,Hiroko Kurokawa,Daniel C. Laughlin,Tali D. Lee,Michelle R. Leishman,Frederic Lens,Tanja Lenz,Simon L. Lewis,Jon Lloyd,Jon Lloyd,Joan Llusià,Frédérique Louault,Siyan Ma,Miguel D. Mahecha,Peter Manning,Tara Joy Massad,Belinda E. Medlyn,Julie Messier,Angela T. Moles,Sandra Cristina Müller,Karin Nadrowski,Shahid Naeem,Ülo Niinemets,S. Nöllert,A. Nüske,Romà Ogaya,Jacek Oleksyn,Vladimir G. Onipchenko,Yusuke Onoda,Jenny C. Ordoñez,Gerhard E. Overbeck,Wim A. Ozinga,Sandra Patiño,Susana Paula,Juli G. Pausas,Josep Peñuelas,Oliver L. Phillips,Valério D. Pillar,Hendrik Poorter,Lourens Poorter,Peter Poschlod,Andreas Prinzing,Raphaël Proulx,Anja Rammig,Sabine Reinsch,Björn Reu,Lawren Sack,Beatriz Salgado-Negret,Jordi Sardans,Satomi Shiodera,Bill Shipley,Andrew Siefert,Enio E. Sosinski,Jean-François Soussana,Emily Swaine,Nathan G. Swenson,Ken Thompson,Peter E. Thornton,Matthew S. Waldram,Evan Weiher,Michael T. White,S. White,S. J. Wright,Benjamin Yguel,Sönke Zaehle,Amy E. Zanne,Christian Wirth +136 more
TL;DR: TRY as discussed by the authors is a global database of plant traits, including morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs, which can be used for a wide range of research from evolutionary biology, community and functional ecology to biogeography.
References
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Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems
TL;DR: This paper proposes informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies, and proposes new user-centric directions for evaluating recommender systems.
Journal ArticleDOI
Probabilistic relevance ranking for collaborative filtering
TL;DR: It is argued that collaborative filtering can be directly cast as a relevance ranking problem, and the basic formula is extended by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance), which largely alleviates the data sparsity problem.
Proceedings ArticleDOI
Imputation-boosted collaborative filtering using machine learning classifiers
TL;DR: A framework of imputation-boosted collaborative filtering (IBCF), which first uses an imputation technique, or perhaps machine learned classifier, to fill-in the sparse user-item rating matrix, then runs a traditional Pearson correlation-based CF algorithm on this matrix to predict a novel rating.
Dissertation
Merchant differentiation through integrative negotiation in agent-mediated electronic commerce
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Proceedings ArticleDOI
Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts
TL;DR: This paper proposes two hybrid CF algorithms, sequential mixture CF and joint mixture CF, each combining advice from multiple experts for effective recommendation, and shows that these algorithms outperform their peers, especially when the underlying data are very sparse.