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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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Proceedings ArticleDOI

Deep Neural Networks for YouTube Recommendations

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TRY - a global database of plant traits

Jens Kattge, +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 ArticleDOI

Discriminative parameter learning of general Bayesian network classifiers

TL;DR: It is shown that ELR does not produce better classifiers with GBN, when the training data is not sufficient for the GBN structure learner to produce a good model, and empirical studies suggest that the better the BN structure is, the less advantages ELR has over OFE, for classification purposes.
Proceedings Article

A Mixture Imputation-Boosted Collaborative Filter

TL;DR: This paper applies several standard imputation techniques within the framework of imputation-boosted collaborative filtering (IBCF), and proposes a novel mixture IBCF algorithm that uses either naive Bayes or mean imputation, depending on the sparsity of the original CF rating dataset.
Journal ArticleDOI

Knowledge discovery in distributed databases using evidence theory

TL;DR: This paper study linguistic summaries and their applications to knowledge discovery in distributed databases using fuzzy set theory and evidence theory to define summaries.
Journal ArticleDOI

Improving the prediction performance of customer behavior through multiple imputation

TL;DR: This study is designed to introduce the multiple imputation technique and show two experimental works of several imputation methods applied to the real cases in electronic customer relationship management domain, the first with missing covariates and the second with missing targets.
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