Evaluating Multi-Model (Metadata-Semantic) Information Retrieval System.

摘要: Abstract—In this paper, we present a variety of methodsto evaluate multi-model information retrieval system. Theevaluation methods are divided into three steps: (1) evaluatinga metadata driven search engine, (2) evaluating personalizedcluster-based semantic and (3) dualrepresentation the user profile for personalized Websearch in an evolving domain. The first part paper presentsan overview most popular models andtheir evaluation methods. second is dedicated to thespecific that used each modelof our platform. This platform already being by onlinestudents at WKU. As 2006 “HyperManyMedia” searchengine has been ranked number 24 on “The Ultimate Guide toUsing Open Courseware 1 ” (between Cambridge University andHarvard Business).Index Terms—Evaluation Methods, Precision, Recall, Informa-tion Retrieval I. I NTRODUCTION What information? [7] cate-gories: information-as-process, information-as-knowledge, andinformation-as-thing. It stated “information anythingthat can change person’s knowledge [7].”[16] looked system from user’sprospective. His framework human centered, where “theinformation seeker defines task, controls interactionwith system, examines extracts relevant in-formation, assesses progress, determines when theinformation-seeking complete [16]”. thispaper based student-centered approach, studentsearches information, retrieves infor-mation student’s query, student uses informationand, end, evaluates its relevance.[14] distinguished between Data Wisdom. isequally available any user, it received, stored retrieved