5,446 research outputs found
Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering
We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.algorithms;probabilistic latent semantic analysis;hybrid recommender systems;recommender systems
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top- recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.Comment: In Proc. of 6th Australasian Data Mining Conference (AusDM), Gold
Coast, Australia, pages 195--202, 200
Multidimensional Membership Mixture Models
We present the multidimensional membership mixture (M3) models where every
dimension of the membership represents an independent mixture model and each
data point is generated from the selected mixture components jointly. This is
helpful when the data has a certain shared structure. For example, three unique
means and three unique variances can effectively form a Gaussian mixture model
with nine components, while requiring only six parameters to fully describe it.
In this paper, we present three instantiations of M3 models (together with the
learning and inference algorithms): infinite, finite, and hybrid, depending on
whether the number of mixtures is fixed or not. They are built upon Dirichlet
process mixture models, latent Dirichlet allocation, and a combination
respectively. We then consider two applications: topic modeling and learning 3D
object arrangements. Our experiments show that our M3 models achieve better
performance using fewer topics than many classic topic models. We also observe
that topics from the different dimensions of M3 models are meaningful and
orthogonal to each other.Comment: 9 pages, 7 figure
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