Probabilistic Sequence Modeling for Recommender Systems

Nicola Barbieri, Antonio Bevacqua, Marco Carnuccio, Giuseppe Manco, Ettore Ritacco

2012

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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