id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_utk4nc3tvjcdfdaejpaqxr2xna	A. Zafra	Multi-instance genetic programming for web index recommendation	2009	10	.pdf	application/pdf	8288	880	66	This article introduces the use of a multi-instance genetic programming algorithm for modelling user The main difficulty in this problem lies in training set representation; web index pages are those which contain references or rules which provide information on whether any of the links contained on a given web index page are of interest to a given user. Section 4 presents Web Index Recommendation as a multi-instance learning Pao, Chuang, Xu, and Fu (2008) have proposed an EM based learning algorithm to provide a comprehensive procedure to maximize of data contained in the instances analyzed (in Section 5 we will describe the format of the clauses used in the web index recommendation problem). Based on previously mentioned considerations, we have considered the use of multi-instance representation for web index recommendation problems. In order to evaluate our G3P-MI algorithm in the web index recommendation task, and to compare with other results previously	./cache/work_utk4nc3tvjcdfdaejpaqxr2xna.pdf	./txt/work_utk4nc3tvjcdfdaejpaqxr2xna.txt