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Conjugate-prior-regularized multinomial pLSA for collaborative filtering

Klasson, Marcus ; Adalbjörnsson, Stefan Ingi LU ; Swärd, Johan LU and Andersen, Søren Vang LU (2017) 25th European Signal Processing Conference, EUSIPCO 2017 2017-January. p.2501-2505
Abstract

We consider the over-fitting problem for multinomial probabilistic Latent Semantic Analysis (pLSA) in collaborative filtering, using a regularization approach. For big data applications, the computational complexity is at a premium and we, therefore, consider a maximum a posteriori approach based on conjugate priors that ensure that complexity of each step remains the same as compared to the un-regularized method. In the numerical section, we show that the proposed regularization method and training scheme yields an improvement on commonly used data sets, as compared to previously proposed heuristics.

Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Collaborative filtering, Conjugate prior regularization, Probabilistic latent semantic analysis, Recommender systems
host publication
25th European Signal Processing Conference, EUSIPCO 2017
volume
2017-January
article number
8081661
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
25th European Signal Processing Conference, EUSIPCO 2017
conference location
Kos, Greece
conference dates
2017-08-28 - 2017-09-02
external identifiers
  • scopus:85041483446
ISBN
9780992862671
DOI
10.23919/EUSIPCO.2017.8081661
language
English
LU publication?
yes
id
6eaaa5fe-b16e-41cf-9930-f4968566e009
date added to LUP
2018-02-22 09:57:44
date last changed
2025-04-04 14:08:41
@inproceedings{6eaaa5fe-b16e-41cf-9930-f4968566e009,
  abstract     = {{<p>We consider the over-fitting problem for multinomial probabilistic Latent Semantic Analysis (pLSA) in collaborative filtering, using a regularization approach. For big data applications, the computational complexity is at a premium and we, therefore, consider a maximum a posteriori approach based on conjugate priors that ensure that complexity of each step remains the same as compared to the un-regularized method. In the numerical section, we show that the proposed regularization method and training scheme yields an improvement on commonly used data sets, as compared to previously proposed heuristics.</p>}},
  author       = {{Klasson, Marcus and Adalbjörnsson, Stefan Ingi and Swärd, Johan and Andersen, Søren Vang}},
  booktitle    = {{25th European Signal Processing Conference, EUSIPCO 2017}},
  isbn         = {{9780992862671}},
  keywords     = {{Collaborative filtering; Conjugate prior regularization; Probabilistic latent semantic analysis; Recommender systems}},
  language     = {{eng}},
  month        = {{10}},
  pages        = {{2501--2505}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Conjugate-prior-regularized multinomial pLSA for collaborative filtering}},
  url          = {{http://dx.doi.org/10.23919/EUSIPCO.2017.8081661}},
  doi          = {{10.23919/EUSIPCO.2017.8081661}},
  volume       = {{2017-January}},
  year         = {{2017}},
}