- title = "Adaptive Web Search: Evolving a Program That Finds Information",
- author = "Michael Gordon and Weiguo (Patrick) Fan and Praveen Pathak",
- journal = "IEEE Intelligent Systems",
- year = "2006",
- volume = "21",
- number = "5",
- pages = "72--77",
- month = sep # "-" # oct,
- keywords = "genetic algorithms, genetic programming, Internet, information needs, relevance feedback, search engines, Web pages, adaptive Web search, document relevance feedback, genetic programming, retrieval algorithms, retrieval technique, search engines, user judgement feedback, user persistent information needs",
- ISSN = "1541-1672",
-
DOI = "
doi:10.1109/MIS.2006.86",
- size = "6 pages",
- abstract = "Anyone who's used a computer to find information on the Web knows that the experience can be frustrating. Search engines are incorporating new techniques (such as examining document link structures) to increase effectiveness. However, searchers all too often face one of two outcomes: reviewing many more Web pages than they'd prefer or failing to find as much useful information as they really want. We introduce a new retrieval technique that exploits users' persistent information needs. These users might include business analysts specialising in genetic technologies, stockbrokers keeping abreast of wireless communications, and legislators needing to understand computer privacy and security developments. To help such searchers, we evolve effective search programs by using feedback based on users' judgments about the relevance of the documents they've retrieved. This approach uses genetic programming to automatically evolve new retrieval algorithms based on a user's evaluation of previously viewed documents",
-
notes = "IR, cosine nearness measure, keyword weighting.
Log. Pop=200. TREC 80000 documents. Large number (500) papers returned to user.
GP way better in comparison with SMART (Singhal, 1996) and ANN.",
