Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Look back, look around: a systematic analysis of effective predictors
  for new outlinks in focused Web crawling   Small and medium enterprises rely on detailed Web analytics to be informed
about their market and competition. Focused crawlers meet this demand by
crawling and indexing specific parts of the Web. Critically, a focused crawler
must quickly find new pages that have not yet been indexed. Since a new page
can be discovered only by following a new outlink, predicting new outlinks is
very relevant in practice. In the literature, many feature designs have been
proposed for predicting changes in the Web. In this work we provide a
structured analysis of this problem, using new outlinks as our running
prediction target. Specifically, we unify earlier feature designs in a
taxonomic arrangement of features along two dimensions: static versus dynamic
features, and features of a page versus features of the network around it.
Within this taxonomy, complemented by our new (mainly, dynamic network)
features, we identify best predictors for new outlinks. Our main conclusion is
that most informative features are the recent history of new outlinks on a page
itself, and on its content-related pages. Hence, we propose a new 'look back,
look around' (LBLA) model, that uses only these features. With the obtained
predictions, we design a number of scoring functions to guide a focused crawler
to pages with most new outlinks, and compare their performance. Interestingly,
the LBLA approach proved extremely effective, outperforming even the models
that use a most complete set of features. One of the learners we use, is the
recent NGBoost method that assumes a Poisson distribution for the number of new
outlinks on a page, and learns its parameters. This connects the two so far
unrelated avenues in the literature: predictions based on features of a page,
and those based on probabilistic modeling. All experiments were carried out on
an original dataset, made available by a commercial focused crawler.

16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph