By Christopher Olston, Marc Najork
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Extra info for Web Crawling
Example text
The weight function w(p) is chosen to reflect the purpose of the crawl. For example, if the purpose is to crawl pages about helicopters, one sets w(p) = 1 for pages about helicopters, and w(p) = 0 for all other pages. 203 204 Batch Crawl Ordering Fig. 1 Weighted coverage (WC) as a function of time elapsed (t) since the beginning of a batch crawl. 1 shows some hypothetical WC curves. Typically, w(p) ≥ 0, and hence WC(t) is monotonic in t. Under a random crawl ordering policy, WC(t) is roughly linear in t; this line serves as a baseline upon which other policies strive to improve.
Fetterly et al. [58] evaluated four crawl ordering policies (breadthfirst; prioritize by indegree; prioritize by trans-domain indegree; 208 Batch Crawl Ordering prioritize by PageRank) under two relevance metrics: • MaxNDCG: The total Normalized Distributed Cumulative Gain (NDCG) [79] score of a set of queries evaluated over the crawled pages, assuming optimal ranking. • Click count: The total number of clicks the crawled pages attracted via a commercial search engine in some time period. The main findings were that prioritization by PageRank is the most reliable and effective method on these metrics, and that imposing perdomain page limits boosts effectiveness.
The reason given by Baeza-Yates et al. [9] for poor performance is that it is overly greedy in going after highindegree pages, and therefore it takes a long time to find pages that have high indegree and PageRank yet are only discoverable via low-indegree pages. The two studies are over different web collections that differ in size by an order of magnitude, and are seven years apart in time. In addition to the aforementioned results, Baeza-Yates et al. [9] proposed a crawl policy that gives priority to sites containing a large number of discovered but uncrawled URLs.