Conjugate priors for Gaussian emission plsa recommender systems
Adalbjörnsson, Stefan LU ; Swärd, Johan LU ; Berg, Magnus Örn ; Andersen, Søren Vang LU and Jakobsson, Andreas LU
(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:
https://lup.lub.lu.se/record/39791dc4-7bc6-4cbe-99ab-83ade70ec89b
- author
- Adalbjörnsson, Stefan
LU
; Swärd, Johan
LU
; Berg, Magnus Örn
; Andersen, Søren Vang
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2016-11-28
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Collaborative filtering, Probabilistic matrix factorization, Recommender systems
- host publication
- 2016 24th European Signal Processing Conference, EUSIPCO 2016
- volume
- 2016-November
- article number
- 7760618
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 24th European Signal Processing Conference, EUSIPCO 2016
- conference location
- Budapest, Hungary
- conference dates
- 2016-08-28 - 2016-09-02
- 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
- 2022-03-01 18:31:55
@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}},
keywords = {{Collaborative filtering; Probabilistic matrix factorization; Recommender systems}},
language = {{eng}},
month = {{11}},
pages = {{2096--2100}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Conjugate priors for Gaussian emission plsa recommender systems}},
url = {{http://dx.doi.org/10.1109/EUSIPCO.2016.7760618}},
doi = {{10.1109/EUSIPCO.2016.7760618}},
volume = {{2016-November}},
year = {{2016}},
}