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update duration is six hours and the urgent update duration should be one hour.
The urgency of channel c which article a belongs to is evaluated as follows:
(4.12)
(4.13)
where
σ c is the ratio of update frequency of channel c, D c is the default duration of
channel c, d c is the latest duration of channel c, and
λ 1 is a weight value.
4.4 Filtering based on user profile and time-series fatures
Based on the user profile and the time-series features, we give a new filtering
model for the news articles. In this model, the filter has three functions: (a) user
profile matching, (b) channel update frequency monitoring, (c) popularity and
freshness calculating. When the user profile for the filtered channel is q, the score
of article a via channel c is calculated by following equation:
where sim(a, q) is the similarity between article a and user profile q, freq(a) is the
urgency of channel c which article a belongs to, pop(a) is the popularity of
α
, fresh(a)
is the freshness of article a .
α
', ß',
γ
', µ, and v are weight values for each term.
5 Concluding Remarks
In this paper, we focussed on the substantial aspects of data broadcasting systems.
We noticed:
Continuous delivery of content, in which update frequencies of their
components may vary, may lead to some temporal inconsistency.
Data content may be desirable to be reorganized into forms suitable for
passive viewing.
It is now a crucial issue how to obtain fresh or popular information from
data broadcasting systems.
As for the first aspect, we introduced a version management mechanism to maintain
the temporal consistency of delivered data by binary version trees for both of
content updates and valid time updates. As for the second aspect, we described a
way to view Web contents in a passive manner like TV-program contents. Our
automatic transformation function from Web pages into TV-prigram-like contents
raised an interesting data reorganization problem. That is, how can we tranform
Web page content into synchronized data streams? The automatic transformation
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