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A survey of collaborative filtering techniques

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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.

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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.
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Link prediction in complex networks: A survey

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TRY - a global database of plant traits

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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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Journal ArticleDOI

A personalised TV listings service for the digital TV age

TL;DR: The development of the PTV (Personalised Television Listings—http://www.ptv.ie) system is described which tackles the information overload associated with modern TV listings data, by providing an Internet-based personalised listings service.
Proceedings ArticleDOI

Investigation of various matrix factorization methods for large recommender systems

TL;DR: An incremental variant of MF is described that efficiently handles new users/ratings, which is crucial in a real-life recommender system and a momentum-based MF approach is introduced that approximates the features by using positive values for either users or items.
Proceedings Article

A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains

TL;DR: This work develops a maximum entropy (maxent) approach to generating recommendations in the context of a user's current navigation stream, suitable for environments where data is sparse, high-dimensional, and dynamic— conditions typical of many recommendation applications.
Journal ArticleDOI

Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers

TL;DR: In this article, the authors propose a discriminative parameter learning approach to find the BN that maximizes a different objective function, i.e., likelihood, rather than classification accuracy.
Journal ArticleDOI

An electronic infrastructure for a virtual university

TL;DR: The Electronic Education Environment, or E3, an infrastructure developed at The University of Texas at Austin to support processes in a virtual university (VU), focuses on developing skills and expertise by mass customizing content on demand rather than providing terminal degree programs with homogeneous and predetermined curricula.
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