An adaptive focused Web crawling algorithm based on learning automata

The recent years have witnessed the birth and explosive growth of the Web. The exponential growth of the Web has made it into a huge source of information wherein finding a document without an efficient search engine is unimaginable. Web crawling has become an important aspect of the Web search on which the performance of the search engines is strongly dependent. Focused Web crawlers try to focus the crawling process on the topic-relevant Web documents. Topic oriented crawlers are widely used in domain-specific Web search portals and personalized search tools. This paper designs a decentralized learning automata-based focused Web crawler. Taking advantage of learning automata, the proposed crawler learns the most relevant URLs and the promising paths leading to the target on-topic documents. It can effectively adapt its configuration to the Web dynamics. This crawler is expected to have a higher precision rate because of construction a small Web graph of only on-topic documents. Based on the Martingale theorem, the convergence of the proposed algorithm is proved. To show the performance of the proposed crawler, extensive simulation experiments are conducted. The obtained results show the superiority of the proposed crawler over several existing methods in terms of precision, recall, and running time. The t-test is used to verify the statistical significance of the precision results of the proposed crawler.