11,397 research outputs found
Predicting Item Popularity: Analysing Local Clustering Behaviour of Users
Predicting the popularity of items in rating networks is an interesting but
challenging problem. This is especially so when an item has first appeared and
has received very few ratings. In this paper, we propose a novel approach to
predicting the future popularity of new items in rating networks, defining a
new bipartite clustering coefficient to predict the popularity of movies and
stories in the MovieLens and Digg networks respectively. We show that the
clustering behaviour of the first user who rates a new item gives insight into
the future popularity of that item. Our method predicts, with a success rate of
over 65% for the MovieLens network and over 50% for the Digg network, the
future popularity of an item. This is a major improvement on current results.Comment: 25 pages, 11 figure
The supervised hierarchical Dirichlet process
We propose the supervised hierarchical Dirichlet process (sHDP), a
nonparametric generative model for the joint distribution of a group of
observations and a response variable directly associated with that whole group.
We compare the sHDP with another leading method for regression on grouped data,
the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method
on two real-world classification problems and two real-world regression
problems. Bayesian nonparametric regression models based on the Dirichlet
process, such as the Dirichlet process-generalised linear models (DP-GLM) have
previously been explored; these models allow flexibility in modelling nonlinear
relationships. However, until now, Hierarchical Dirichlet Process (HDP)
mixtures have not seen significant use in supervised problems with grouped data
since a straightforward application of the HDP on the grouped data results in
learnt clusters that are not predictive of the responses. The sHDP solves this
problem by allowing for clusters to be learnt jointly from the group structure
and from the label assigned to each group.Comment: 14 page
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