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
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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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Proceedings Article
Maximum-Margin Matrix Factorization
TL;DR: A novel approach to collaborative prediction is presented, using low-norm instead of low-rank factorizations, inspired by, and has strong connections to, large-margin linear discrimination.
Proceedings Article
Learning Collaborative Information Filters
TL;DR: This work proposes a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm, and identifies the shortcomings of current collaborative filtering techniques and proposes the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches.
Proceedings ArticleDOI
Fast maximum margin matrix factorization for collaborative prediction
TL;DR: This work investigates a direct gradient-based optimization method for MMMF and finds that MMMf substantially outperforms all nine methods he tested and demonstrates it on large collaborative prediction problems.
Proceedings ArticleDOI
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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.
Proceedings Article
Recommendation as classification: using social and content-based information in recommendation
TL;DR: This paper presented an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences, and showed that their method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a community of over 250 users.