Web spam pages use various techniques to achieve
higher-than-deserved rankings in a search engine’s
results. While human experts can identify
spam, it is too expensive to manually evaluate a
large number of pages. Instead, we propose techniques
to semi-automatically separate reputable,
good pages from spam. We first select a small set
of seed pages to be evaluated by an expert. Once
we manually identify the reputable seed pages, we
use the link structure of the web to discover other
pages that are likely to be good. In this paper
we discuss possible ways to implement the seed
selection and the discovery of good pages. We
present results of experiments run on the World
Wide Web indexed by AltaVista and evaluate the
performance of our techniques. Our results show
that we can effectively filter out spam from a significant
fraction of the web, based on a good seed
set of less than 200 sites.
LoopNet is the largest commercial real estate listing service online. Search the largest database of commercial real estate for sale or for lease, post your property listings and market them to the largest principals audience, save buying or leasing se...
D. Schlör, J. Pfister, and A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), page 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)
D. Wangsadirdja, J. Pfister, K. Kobs, and A. Hotho. Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), page 1090--1095. Toronto, Canada, Association for Computational Linguistics, (July 2023)