8 research outputs found
Social and content hybrid image recommender system for mobile social networks
One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Clustering in high-dimensional spaces is nowadays a recurrent problem in many
scientific domains but remains a difficult task from both the clustering
accuracy and the result understanding points of view. This paper presents a
discriminative latent mixture (DLM) model which fits the data in a latent
orthonormal discriminative subspace with an intrinsic dimension lower than the
dimension of the original space. By constraining model parameters within and
between groups, a family of 12 parsimonious DLM models is exhibited which
allows to fit onto various situations. An estimation algorithm, called the
Fisher-EM algorithm, is also proposed for estimating both the mixture
parameters and the discriminative subspace. Experiments on simulated and real
datasets show that the proposed approach performs better than existing
clustering methods while providing a useful representation of the clustered
data. The method is as well applied to the clustering of mass spectrometry
data