Focused crawler

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A focused crawler or topical crawler is a web crawler that attempts to download only web pages that are relevant to a pre-defined topic or set of topics. Topical crawling generally assumes that only the topic is given, while focused crawling also assumes that some labeled examples of relevant and not relevant pages are available. Topical crawling was first introduced by Menczer [1][2]. Focused crawling was first introduced by Chakrabarti et al.[3].


A focused crawler ideally would like to download only web pages that are relevant to a particular topic and avoid downloading all others.

Therefore a focused crawler may predict the probability that a link to a particular page is relevant before actually downloading the page. A possible predictor is the anchor text of links; this was the approach taken by Pinkerton [4] in a crawler developed in the early days of the Web. In a review of topical crawling algorithms, Menczer et al. [5] show that such simple strategies are very effective for short crawls, while more sophisticated techniques such as reinforcement learning and evolutionary adaptation can give the best performance over longer crawls. Diligenti et al. [6] propose to use the complete content of the pages already visited to infer the similarity between the driving query and the pages that have not been visited yet.

In another approach, the relevance of a page is determined after downloading its content. Relevant pages are sent to content indexing and their contained URLs are added to the crawl frontier; pages that fall below a relevance threshold are discarded.

The performance of a focused crawler depends mostly on the richness of links in the specific topic being searched, and focused crawling usually relies on a general web search engine for providing starting points.


  1. Menczer, F. (1997). ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery. In D. Fisher, ed., Proceedings of the 14th International Conference on Machine Learning (ICML97). Morgan Kaufmann.
  2. Menczer, F. and Belew, R.K. (1998). Adaptive Information Agents in Distributed Textual Environments. In K. Sycara and M. Wooldridge (eds.) Proceedings of the 2nd International Conference on Autonomous Agents (Agents '98). ACM Press.
  3. Chakrabarti, S., van den Berg, M., and Dom, B. (1999). Focused crawling: A new approach to topic-specific web resource discovery. Computer Networks, 31(11–16):1623–1640.
  4. Pinkerton, B. (1994). Finding what people want: Experiences with the WebCrawler. In Proceedings of the First World Wide Web Conference, Geneva, Switzerland.
  5. Menczer, F., Pant, G., and Srinivasan, P. (2004). Topical Web Crawlers: Evaluating Adaptive Algorithms. ACM Trans. on Internet Technology 4(4): 378–419.
  6. Diligenti, M., Coetzee, F., Lawrence, S., Giles, C. L., and Gori, M. (2000). Focused crawling using context graphs. In Proceedings of the 26th International Conference on Very Large Databases (VLDB), pages 527-534, Cairo, Egypt.

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