Look back, look around: A systematic analysis of effective predictors for new outlinks in focused Web crawling

Thi Kim Nhung Dang, Doina Bucur, Berk Atil, Guillaume Pitel, Frank Ruis, Hamidreza Kadkhodaei, Nelly Litvak*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
75 Downloads (Pure)

Abstract

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 of 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. The LBLA approach proved extremely effective, outperforming other models including those 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.
Original languageEnglish
Article number110126
Pages (from-to)1-16
Number of pages16
JournalKnowledge-based systems
Volume260
Early online date17 Nov 2022
DOIs
Publication statusPublished - 25 Jan 2023

Keywords

  • UT-Hybrid-D
  • Focused crawling
  • Web mining
  • Statistical models
  • Probabilistic regression
  • Web search engines
  • Web change prediction

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