A probabilistic model for personalized tag prediction.
KDD, page 959-968. ACM, (2010)Abstract
Social tagging systems have become increasingly popular for sharing
and organizing web resources. Tag recommendation is a common
feature of social tagging systems. Social tagging by nature
is an incremental process, meaning that once a user has saved a
web page with tags, the tagging system can provide more accurate
predictions for the user, based on the user’s incremental behavior.
However, existing tag prediction methods do not consider
this important factor, in which their training and test datasets are either
split by a fixed time stamp or randomly sampled from a larger
corpus. In our temporal experiments, we perform a time-sensitive
sampling on an existing public dataset, resulting in a new scenario
which is much closer to “real-world”.
In this paper, we address the problem of tag prediction by
proposing a probabilistic model for personalized tag prediction.
The model is a Bayesian approach, and integrates three factors—
an ego-centric effect, environmental effects and web page content.
Two methods—both intuitive calculation and learning
optimization—are provided for parameter estimation. Pure graphbased
methods which may have significant constraints (such as every
user, every item and every tag has to occur in at least p posts)
cannot make a prediction in most “real world” cases while our
model improves the F-measure by over 30% compared to a leading
algorithm on a publicly-available real-world dataset.