• Corpus ID: 55823451

A Stochastic Model for Collaborative Recommendation

  title={A Stochastic Model for Collaborative Recommendation},
  author={G{\'e}rard Biau and Beno{\^i}t Cadre and Laurent Rousset Rouviere},
  journal={arXiv: Machine Learning},
Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In its most common form, the problem is framed as trying to estimate ratings for items that have not yet been consumed by a user. Despite wide-ranging literature, little is known… 

Tables from this paper

Application of Random Walks to Decentralized Recommender Systems

This paper proposes a new user-based random walk approach customized for decentralized systems, specifically designed to handle sparse data, and demonstrates that over a wide range of sparsity, the algorithm outperforms other decentralized CF schemes.

Towards a personalized Internet: a case for a full decentralization

  • Anne-Marie Kermarrec
  • Computer Science
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2013
This paper argues that ultra-specific content could be retrieved and disseminated should search and notification be personalized to fit this new setting, and advocates the need for fully decentralized systems.



Item-based collaborative filtering recommendation algorithms

This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.

Restricted Boltzmann machines for collaborative filtering

This paper shows how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies, and demonstrates that RBM's can be successfully applied to the Netflix data set.

Incorporating contextual information in recommender systems using a multidimensional approach

The article presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and

A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

This work presents a general approach for collaborative filtering using spectral regularization to learn linear operators mapping a set of "users" to aSet of possibly desired " objects", and provides novel representer theorems that are used to develop new estimation methods.

A Taxonomy of Recommender Agents on the Internet

A state-of-the-art taxonomy of intelligent recommender agents on the Internet and a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.

Social information filtering: algorithms for automating “word of mouth”

The implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists, and four different algorithms for making recommendations by using social information filtering were tested and compared.

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main

GroupLens: an open architecture for collaborative filtering of netnews

GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.