Title :
A learning algorithm for metasearching using rough set theory
Author :
Ali, Rashid ; Beg, M. M Sufyan
Author_Institution :
Dept. of Comput. Eng., AMU, Aligarh
Abstract :
Metasearching is the process of combining search results of different search systems into a single set of ranked results, which is expected to be better than results of best of the participating search systems. In this paper, we present a supervised learning algorithm for metasearching. Our algorithm learns the ranking rules on the basis of user feedback based metasearching for the queries in the training set. We use rough set theory to mine the ranking rules. The ranking rules are validated using cross validation. The best of the ranking rules is then used to estimate the results of metasearching for the other queries. We compare our method with modified Shimura technique. We claim that our method is more useful than modified Shimura technique as it models userpsilas preference.
Keywords :
learning (artificial intelligence); query processing; rough set theory; search engines; cross validation; metasearch process; modified Shimura technique; participating search engine; query processing; ranking rule mining; rough set theory; supervised learning algorithm; user feedback; Algorithm design and analysis; Costs; Databases; Feedback; Metasearch; Search engines; Set theory; Supervised learning; Terminology; Training data; learning algorithm; metasearching; rough set; user feedback;
Conference_Titel :
Computer and information technology, 2007. iccit 2007. 10th international conference on
Conference_Location :
Dhaka
Print_ISBN :
978-1-4244-1550-2
Electronic_ISBN :
978-1-4244-1551-9
DOI :
10.1109/ICCITECHN.2007.4579421
