A survey of collaborative filtering techniques
Reads0
Chats0
TLDR
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
More filters
Journal ArticleDOI
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.
Journal ArticleDOI
Link prediction in complex networks: A survey
TL;DR: Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
Proceedings ArticleDOI
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
More filters
Proceedings ArticleDOI
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system
TL;DR: The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques and is experimentally validated by showing that even simple filterbots such as spell checking can increase the utility for users of sparsely populated collaborative filtering system.
Patent
Computerized cross-language document retrieval using latent semantic indexing
TL;DR: In this article, a methodology for retrieving textual data objects in a multiplicity of languages is disclosed, where data objects are treated in the statistical domain by presuming that there is an underlying, latent semantic structure in the usage of words in each language under consideration.
Journal ArticleDOI
PocketLens: Toward a personal recommender system
TL;DR: The new PocketLens collaborative filtering algorithm along with five peer-to-peer architectures for finding neighbors are presented and evaluated in a series of offline experiments, showing that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.
Journal ArticleDOI
LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data
TL;DR: A fundamentally new method for generating user profiles that takes advantage of a large-scale database of demographic data to generalize user-specified data along the patterns common across the population, including areas not represented in the user's original data is presented.
Proceedings ArticleDOI
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
TL;DR: It is empirically demonstrated that two of the most acclaimed CF recommendation algorithms have flaws that result in a dramatically unacceptable user experience, and a new Belief Distribution Algorithm is introduced that overcomes these flaws and provides substantially richer user modeling.