Visualization of Collaborative Data

06/27/2012
by   Guobiao Mei, et al.
0

Collaborative data consist of ratings relating two distinct sets of objects: users and items. Much of the work with such data focuses on filtering: predicting unknown ratings for pairs of users and items. In this paper we focus on the problem of visualizing the information. Given all of the ratings, our task is to embed all of the users and items as points in the same Euclidean space. We would like to place users near items that they have rated (or would rate) high, and far away from those they would give a low rating. We pose this problem as a real-valued non-linear Bayesian network and employ Markov chain Monte Carlo and expectation maximization to find an embedding. We present a metric by which to judge the quality of a visualization and compare our results to local linear embedding and Eigentaste on three real-world datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/10/2022

Tensor-based Collaborative Filtering With Smooth Ratings Scale

Conventional collaborative filtering techniques don't take into consider...
research
05/02/2018

Exploring Users' Perception of Collaborative Explanation Styles

Collaborative filtering systems heavily depend on user feedback expresse...
research
02/02/2022

A Recommender System Based on a Double Feature Allocation Model

A collaborative filtering recommender system predicts user preferences b...
research
09/30/2015

Learning From Missing Data Using Selection Bias in Movie Recommendation

Recommending items to users is a challenging task due to the large amoun...
research
01/12/2011

Extracting Features from Ratings: The Role of Factor Models

Performing effective preference-based data retrieval requires detailed a...
research
04/01/2020

Map-Based Visualization of 2D/3D Spatial Data via Stylization and Tuning of Information Emphasis

In Geographical Information search, map visualization can challenge the ...
research
06/18/2012

TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings

This paper revisits the problem of analyzing multiple ratings given by d...

Please sign up or login with your details

Forgot password? Click here to reset