Website replica detection with distant supervision

Abstract

Duplicate content on the Web occurs within the same website or across multiple websites. The latter is mainly associated with the existence of website replicas—sites that are perceptibly similar. Replication may be accidental, intentional or malicious, but no matter the reason, search engines suffer greatly either from unnecessarily storing and moving duplicate data, or from providing search results that do not offer real value to the users. In this paper, we model the detection of website replicas as a pairwise classification problem with distant supervision. That is, (heuristically) finding obvious replica and non-replica cases is trivial, but learning effective classifiers requires a representative set of non-obvious labeled examples, which are hard to obtain. We employ efficient Expectation-Maximization (EM) algorithms in order to find non-obvious examples from obvious ones, enlarging the training-set and improving the classifiers iteratively. Our classifiers employ association rules, being thus incrementally updated as the EM process iterates, making our algorithms time-efficient. Experiments show that: (1) replicas are fully eliminated at a false-positive rate lower than 0.005, incurring in + 19% reduction in the number of duplicate URLs, (2) reduction increases to + 21% by using our site-level algorithms in conjunction with existing URL-level algorithms, and (3) our classifiers are more than two orders of magnitude faster than semi-supervised alternative solutions.