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Conjugate priors for Gaussian emission plsa recommender systems

Adalbjörnsson, Stefan LU ; Swärd, Johan LU ; Berg, Magnus Örn LU ; Andersen, Søren Vang LU and Jakobsson, Andreas LU (2016) 24th European Signal Processing Conference, EUSIPCO 2016 In 2016 24th European Signal Processing Conference, EUSIPCO 2016 2016-November. p.2096-2100
Abstract

Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic latent semantic analysis model, able to overcome the over-fitting problem restricting the performance of the earlier formulation. Furthermore, in experiments using the EachMovie and MovieLens data sets, it is shown that the proposed regularized model achieves significantly improved prediction accuracy of user preferences as compared to the latent semantic analysis model without priors.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Collaborative filtering, Probabilistic matrix factorization, Recommender systems
in
2016 24th European Signal Processing Conference, EUSIPCO 2016
volume
2016-November
pages
5 pages
publisher
European Signal Processing Conference, EUSIPCO
conference name
24th European Signal Processing Conference, EUSIPCO 2016
external identifiers
  • scopus:85006073806
ISBN
9780992862657
DOI
10.1109/EUSIPCO.2016.7760618
language
English
LU publication?
yes
id
39791dc4-7bc6-4cbe-99ab-83ade70ec89b
date added to LUP
2016-12-30 08:11:09
date last changed
2017-03-16 09:33:14
@inproceedings{39791dc4-7bc6-4cbe-99ab-83ade70ec89b,
  abstract     = {<p>Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic latent semantic analysis model, able to overcome the over-fitting problem restricting the performance of the earlier formulation. Furthermore, in experiments using the EachMovie and MovieLens data sets, it is shown that the proposed regularized model achieves significantly improved prediction accuracy of user preferences as compared to the latent semantic analysis model without priors.</p>},
  author       = {Adalbjörnsson, Stefan and Swärd, Johan and Berg, Magnus Örn and Andersen, Søren Vang and Jakobsson, Andreas},
  booktitle    = {2016 24th European Signal Processing Conference, EUSIPCO 2016},
  isbn         = {9780992862657},
  keyword      = {Collaborative filtering,Probabilistic matrix factorization,Recommender systems},
  language     = {eng},
  month        = {11},
  pages        = {2096--2100},
  publisher    = {European Signal Processing Conference, EUSIPCO},
  title        = {Conjugate priors for Gaussian emission plsa recommender systems},
  url          = {http://dx.doi.org/10.1109/EUSIPCO.2016.7760618},
  volume       = {2016-November},
  year         = {2016},
}