NDLI: Optimizing ranking method using social annotations based on language model

Content Provider	Springer Nature Link
Author	Wen, Kunmei Li, Ruixuan Xia, Jing Gu, Xiwu
Copyright Year	2011
Abstract	Recent research has shown that more and more web users utilize social annotations to manage and organize their interested resources. Therefore, with the growing popularity of social annotations, it is becoming more and more important to utilize such social annotations to achieve effective web search. However, using a statistical model, there are no previous studies that examine the relationships between queries and social annotations. Motivated by this observation, we use social annotations to re-rank search results. We intend to optimize retrieval ranking method by using the ranking strategy of integrating the query-annotation similarity into query-document similarity. Specifically, we calculate the query-annotation similarity by using a statistical language model, which in a shorter form we call simply a language model. Then the initial search results are re-ranked according to the computational weighted score of the query-document similarity score and the query-annotation similarity score. Experimental results show that the proposed method can improve the NDCG score by 8.13%. We further conduct an empirical evaluation of the method by using a query set including about 300 popular social annotations and constructed phrases. More generally, the optimized results with social annotations based on a language model can be of significant benefit to web search.
Starting Page	81
Ending Page	96
Page Count	16
File Format	PDF
ISSN	02692821
Journal	Artificial Intelligence Review
Volume Number	41
Issue Number	1
e-ISSN	15737462
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-01-03
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Social annotation Language model Query-annotation similarity Query-document similarity Artificial Intelligence (incl. Robotics) Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Linguistics and Language

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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