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Conjugate-Prior-Regularized Multinomial pLSA for Collaborative Filtering

Klasson, Marcus LU (2016) In Master's Theses in Mathematical Sciences FMA820 20152
Mathematics (Faculty of Engineering)
Abstract
Collaborative filtering is a method for making predictions about consumer interests by collecting preferences or information about opinions from other consumers. For this purpose statistical modeling techniques are applied to learn personalized models for each consumer based on every purchase or provided rating to the available items. Such a technique is probabilistic Latent Semantic Analysis (pLSA), which within this thesis attempts to model consumers into groups based on similarities in movie preferences to improve personalized rating predictions on unseen movies. The main challenge with pLSA in collaborative filtering is the overfitting problem, which results in model parameters that are strictly determined by the past ratings and thus... (More)
Collaborative filtering is a method for making predictions about consumer interests by collecting preferences or information about opinions from other consumers. For this purpose statistical modeling techniques are applied to learn personalized models for each consumer based on every purchase or provided rating to the available items. Such a technique is probabilistic Latent Semantic Analysis (pLSA), which within this thesis attempts to model consumers into groups based on similarities in movie preferences to improve personalized rating predictions on unseen movies. The main challenge with pLSA in collaborative filtering is the overfitting problem, which results in model parameters that are strictly determined by the past ratings and thus gives unreliable predictions for unknown data. To counteract the overfitting a regularization method called conjugate-prior-regularization is proposed to introduce additional information about the proportions of the model parameters. It is shown that the proposed regularization provides more robust learning from sparse datasets and also improves the recommendation performance on discrete ratings. (Less)
Please use this url to cite or link to this publication:
author
Klasson, Marcus LU
supervisor
organization
course
FMA820 20152
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Recommender systems, Information filtering, Latent class models, Machine learning
publication/series
Master's Theses in Mathematical Sciences
report number
LUFTMA-3290-2016
ISSN
1404-6342
other publication id
2016:E12
language
English
id
8879554
date added to LUP
2016-08-25 13:53:23
date last changed
2016-08-25 13:53:23
@misc{8879554,
  abstract     = {Collaborative filtering is a method for making predictions about consumer interests by collecting preferences or information about opinions from other consumers. For this purpose statistical modeling techniques are applied to learn personalized models for each consumer based on every purchase or provided rating to the available items. Such a technique is probabilistic Latent Semantic Analysis (pLSA), which within this thesis attempts to model consumers into groups based on similarities in movie preferences to improve personalized rating predictions on unseen movies. The main challenge with pLSA in collaborative filtering is the overfitting problem, which results in model parameters that are strictly determined by the past ratings and thus gives unreliable predictions for unknown data. To counteract the overfitting a regularization method called conjugate-prior-regularization is proposed to introduce additional information about the proportions of the model parameters. It is shown that the proposed regularization provides more robust learning from sparse datasets and also improves the recommendation performance on discrete ratings.},
  author       = {Klasson, Marcus},
  issn         = {1404-6342},
  keyword      = {Recommender systems,Information filtering,Latent class models,Machine learning},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Conjugate-Prior-Regularized Multinomial pLSA for Collaborative Filtering},
  year         = {2016},
}