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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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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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Proceedings ArticleDOI

Mining navigation history for recommendation

TL;DR: A system which actively monitors and tracks a user's navigation and applies data mining techniques to discover the hidden knowledge contained in the history, which is then used to suggest potentially interesting web pages to users.
Journal ArticleDOI

A graph model for E-commerce recommender systems

TL;DR: A graph model is developed that provides a generic data representation and can support different recommendation methods and showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information.
Proceedings ArticleDOI

Taxonomy-driven computation of product recommendations

TL;DR: Relationships between super-concepts and sub- Concepts constitute an important cornerstone of the novel approach, providing powerful inference opportunities for profile generation based upon the classification of products that customers have chosen.

Improved Neighborhood-based Collaborative Filtering

TL;DR: This work enhances the neighborhood-based approach leading to a substantial improvement of prediction accuracy, without a meaningful increase in running time, and shows how to simultaneously derive interpolation weights for all nearest neighbors.

Collaborative Filtering: A Machine Learning Perspective

TL;DR: This thesis is a comprehensive study of rating-based, pure, non-sequential collaborative filtering, and implements a total of nine prediction methods, and conducts large scale prediction accuracy experiments.
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