Evaluation of Reranked Recommended Queries in Web Information Retrieval using NDCG and CV | Zendy

R. Umagandhi | Zendy; Ashwani Kumar | Zendy

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Evaluation of Reranked Recommended Queries in Web Information Retrieval using NDCG and CV

Author(s) -

R. Umagandhi,

Ashwani Kumar

Publication year - 2015

Publication title -

international journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2074-9015

pISSN - 2074-9007

DOI - 10.5815/ijitcs.2015.08.04

Subject(s) - computer science , information retrieval , ranking (information retrieval) , learning to rank , set (abstract data type) , search engine , variance (accounting) , ambiguity , query expansion , data mining , accounting , business , programming language

Tremendous growth of the Web, lack of background\udknowledge about the Information Retrieval (IR), length of the\udinput query keywords and its ambiguity, Query\udRecommendation is an important procedure which analyzes the\udreal search intent of the user and recommends set of queries to\udbe used in future to retrieve the relevant and required\udinformation. The proposed method recommends the queries by\udgenerating frequently accessed queries, rerank the\udrecommended queries and evaluates the recommendation with\udthe help of the ranking measures Normalized Discounted\udCumulative Gain (NDCG) and Coefficient of Variance (CV).\udThe proposed strategies are experimentally evaluated using real\udtime American On Line (AOL) search engine query log

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