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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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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

The multiple multiplicative factor model for collaborative filtering

TL;DR: Empirical results from the collaborative filtering domain are presented showing that a binary/multinomial MMF model matches the performance of the best existing models while learning an interesting latent space description of the users.
Proceedings Article

Incremental methods for computing bounds in partially observable Markov decision processes

TL;DR: Novel incremental versions of grid-based linear interpolation method and simple lower bound method with Sondik's updates are introduced and a new method for computing an initial upper bound - the fast informed bound method is introduced.
Book ChapterDOI

Learning a model of a web user's interests

TL;DR: This paper presents a novel method for learning a model of the user's browsing behavior from a set of annotated web logs, and describes the enhanced browser, aie, that is designed and implemented for collecting these annotatedweb logs.
Proceedings Article

Instance Selection Techniques for Memory-based Collaborative Filtering.

TL;DR: This work focuses on a typical user preference database that contains many missing values, and proposes four novel instance reduction techniques called TURF1-TURF4 as a preprocessing step to improve the efficiency and accuracy of the memory-based CF algorithm.
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