5,165 research outputs found
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-measure with a nested structure to accommodate
content and context observations at multiple levels. The proposed model
possesses properties that link the nested Dirichlet processes (nDP) and the
Dirichlet process mixture models (DPM) in an interesting way: integrating out
all contents results in the DPM over contexts, whereas integrating out
group-specific contexts results in the nDP mixture over content variables. We
provide a Polya-urn view of the model and an efficient collapsed Gibbs
inference procedure. Extensive experiments on real-world datasets demonstrate
the advantage of utilizing context information via our model in both text and
image domains.Comment: Full version of ICML 201
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes
This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception
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