DeepAI
Log In Sign Up

Using Wikipedia to Boost SVD Recommender Systems

12/05/2012
by   Gilad Katz, et al.
0

Singular Value Decomposition (SVD) has been used successfully in recent years in the area of recommender systems. In this paper we present how this model can be extended to consider both user ratings and information from Wikipedia. By mapping items to Wikipedia pages and quantifying their similarity, we are able to use this information in order to improve recommendation accuracy, especially when the sparsity is high. Another advantage of the proposed approach is the fact that it can be easily integrated into any other SVD implementation, regardless of additional parameters that may have been added to it. Preliminary experimental results on the MovieLens dataset are encouraging.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/17/2019

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

With the abundance of data in recent years, interesting challenges are p...
01/02/2021

A Survey of Latent Factor Models for Recommender Systems and Personalization

Recommender systems aim to personalize the experience of a user and are ...
04/13/2018

Regularized Singular Value Decomposition and Application to Recommender System

Singular value decomposition (SVD) is the mathematical basis of principa...
10/05/2021

Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems

Graph Representation Learning (GRL) is an upcoming and promising area in...
12/17/2020

Adaptive Multi-Agent E-Learning Recommender Systems

Educational recommender systems have become a necessity in the recent ye...
10/03/2019

Quantum tensor singular value decomposition with applications to recommendation systems

In this paper, we present a quantum singular value decomposition algorit...
02/15/2020

Sparse Coresets for SVD on Infinite Streams

In streaming Singular Value Decomposition (SVD), d-dimensional rows of a...