This paper introduces a novel link-based ranking algorithm based on a model of focused Web surfers. FocusedRank is described and compared to implementations of PageRank and Topic-Sensitive PageRank. We report a user study that measures the relevance and precision of each approach. FocusedRank gives superior relevancy over PageRank, while significantly reducing the computational complexity compared to the Topic-Senstivice PageRank.

A collaborative crawler is a group of crawling nodes, in which each crawling node is responsible for a specific portion of the web. We study the problem of collecting geographi-cally-aware pages using collaborative crawling strategies. We first propose several collaborative crawling strategies for the geographically focused crawling, whose goal is to collect web pages about specified geographic locations, by considering features like URL address of page, content of page, extended anchor text of link, and others. Later, we propose various evaluation criteria to qualify the performance of such crawling strategies. Finally, we experimentally study our crawling strategies by crawling the real web data showing that some of our crawling strategies greatly outperform the simple URL-hash based partition collaborative crawling, in which the crawling assignments are determined according to the hash-value computation over URLs. More precisely, features like URL address of page and extended anchor text of link are shown to yield the best overall performance for the geographically focused crawling.

We propose a novel method for document clustering using character N-grams. In the traditional vector-space model, the documents are represented as vectors, in which each dimension corresponds to a word. We propose a document representation based on the most frequent character N-grams, with window size of up to 10 characters. We derive a new distance measure, which produces uniformly better results when compared to the word-based and term-based methods. The result becomes more significant in the light of the robustness of the N-gram method with no language-dependent preprocessing. Experiments on the performance of a clustering algorithm on a variety of test document corpora demonstrate that the N-gram representation with n=3 outperforms both word and term representations. The comparison between word and term representations depends on the data set and the selected dimensionality.