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Authors: Nicola Barbieri 1 ; Antonio Bevacqua 2 ; Marco Carnuccio 2 ; Giuseppe Manco 3 and Ettore Ritacco 3

Affiliations: 1 University of Calabria and Italian National Research Council, Italy ; 2 University of Calabria, Italy ; 3 Italian National Research Council, Italy

ISBN: 978-989-8565-29-7

Keyword(s): Recommender Systems, Collaborative Filtering, Probabilistic Topic Models, Performance.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaborative Filtering ; Computational Intelligence ; Data Mining in Electronic Commerce ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining High-Dimensional Data ; Soft Computing ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main features of these models is that generative process follows a bag-of-words assumption, i.e each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by exploiting a conditional Markovian assumptions, where the token generation depends on the current topic and on the previous token. The resulting model is capable of accommodating temporal correlations among tokens, which better model user behavior. This is particularly significant in a collaborative filtering context, where the choice of a user can be exploited for recommendation purposes, and hence a more realistic and accurate modeling enables better recommendations. For the mentioned model we present a fast Gibbs Sampling procedure for the parameters estimation. A thorough experimental evaluation over real-word data shows the performance advantages, in terms of recall and precision, of the proposed sequence-modeling approach. (More)

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Paper citation in several formats:
Barbieri, N.; Bevacqua, A.; Carnuccio, M.; Manco, G. and Ritacco, E. (2012). Probabilistic Sequence Modeling for Recommender Systems.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 75-84. DOI: 10.5220/0004140700750084

@conference{kdir12,
author={Nicola Barbieri. and Antonio Bevacqua. and Marco Carnuccio. and Giuseppe Manco. and Ettore Ritacco.},
title={Probabilistic Sequence Modeling for Recommender Systems},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={75-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004140700750084},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Probabilistic Sequence Modeling for Recommender Systems
SN - 978-989-8565-29-7
AU - Barbieri, N.
AU - Bevacqua, A.
AU - Carnuccio, M.
AU - Manco, G.
AU - Ritacco, E.
PY - 2012
SP - 75
EP - 84
DO - 10.5220/0004140700750084

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