Effectiveness of search result classification based on relevance feedback

Autor:	Kalervo Järvelin, Feza Baskaya, Heikki Keskustalo
Rok vydání:	2013
Předmět:	Information retrieval Computer science Process (engineering) Relevance feedback Library and Information Sciences Space (commercial competition) computer.software_genre Term (time) Reduction (complexity) Classification methods Research questions Data mining computer Information Systems
Zdroj:	Journal of Information Science. 39:764-772
ISSN:	1741-6485 0165-5515
DOI:	10.1177/0165551513488317
Popis:	Relevance feedback (RF) has been studied under laboratory conditions using test collections and either test persons or simple simulation. These studies have given mixed results. Automatic (or pseudo) RF and intellectual RF, both leading to query reformulation, are the main approaches to explicit RF. In the present study we perform RF with the help of classification of search results. We conduct our experiments in a comprehensive collection, namely various TREC ad-hoc collections with 250 topics. We also studied various term space reduction techniques for the classification process. The research questions are: given RF on top results of pseudo RF (PRF) query results, is it possible to learn effective classifiers for the following results? What is the effectiveness of various classification methods? Our findings indicate that this approach of applying RF is significantly more effective than PRF with short (title) queries and long (title and description) queries.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d343843fe816a0890d2422b916292ee6 https://doi.org/10.1177/0165551513488317 Zobrazit plný text záznamu