APSIPA Transactions on Signal and Information Processing > Vol 4 > Issue 1

Blind compressive sensing formulation incorporating metadata for recommender system design

Anupriya Gogna, Indraprastha Institute of Information Technology, India, anupriyag@iiitd.ac.in , Angshul Majumdar, Indraprastha Institute of Information Technology, India
 
Suggested Citation
Anupriya Gogna and Angshul Majumdar (2015), "Blind compressive sensing formulation incorporating metadata for recommender system design", APSIPA Transactions on Signal and Information Processing: Vol. 4: No. 1, e2. http://dx.doi.org/10.1017/ATSIP.2015.6

Publication Date: 20 Jul 2015
© 2015 Anupriya Gogna and Angshul Majumdar
 
Subjects
 
Keywords
Compressed sensingCollaborative filteringMatrix factorization
 

Share

Open Access

This is published under the terms of the Creative Commons Attribution licence.

Downloaded: 561 times

In this article:
I. INTRODUCTION 
II. RELATED WORK 
III. PROPOSED FORMULATION AND ALGORITHM DESIGN 
IV. EXPERIMENTAL SETUP AND RESULTS 
V. CONCLUSION 

Abstract

Standard techniques in matrix factorization (MF) – a popular method for latent factor model-based design – result in dense matrices for both users and items. Users are likely to have some affinity toward all the latent factors – making a dense matrix plausible, but it is not possible for the items to possess all the latent factors simultaneously; hence it is more likely to be sparse. Therefore, we propose to factor the rating matrix into a dense user matrix and a sparse item matrix, leading to the blind compressed sensing (BCS) framework. To further enhance the prediction quality of our design, we aim to incorporate user and item metadata into the BCS framework. The additional information helps in reducing the underdetermined nature of the problem of rating prediction caused by extreme sparsity of the rating dataset. Our design is based on the belief that users sharing similar demographic profile have similar preferences and thus can be described by the similar latent factor vectors. We also use item metadata (genre information) to group together the similar items. We modify our BCS formulation to include item metadata under the assumption that items belonging to common genre share similar sparsity pattern. We also design an efficient algorithm to solve our formulation. Extensive experimentation conducted on the movielens dataset validates our claim that our modified MF framework utilizing auxiliary information improves upon the existing state-of-the-art techniques.

DOI:10.1017/ATSIP.2015.6