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Item-based collaborative filtering recommendation algorithms
TLDR
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. Expand
Evaluating collaborative filtering recommender systems
TLDR
The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. Expand
The MovieLens Datasets: History and Context
TLDR
The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented. Expand
An algorithmic framework for performing collaborative filtering
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercialExpand
Improving recommendation lists through topic diversification
TLDR
This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists. Expand
GroupLens: applying collaborative filtering to Usenet news
TLDR
The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high. Expand
Analysis of recommendation algorithms for e-commerce
TLDR
This paper investigates several te hniques for analyzing large-s ale pur hase and preferen e data for the purpose of producing useful re ommendations to ustomers and devise and apply their ombinations on the authors' data sets to ompare for re Ommendation quality and performan e. Expand
Application of Dimensionality Reduction in Recommender System - A Case Study
TLDR
This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions. Expand
Explaining collaborative filtering recommendations
TLDR
This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process. Expand
Building Successful Online Communities: Evidence-Based Social Design
Online communities are among the most popular destinations on the Internet, but not all online communities are equally successful. For every flourishing Facebook, there is a moribund Friendster--notExpand
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