Estimating intent types for search result diversification

Kosetsu Tsukuda, Tetsuya Sakai, Zhicheng Dou, Katsumi Tanaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Given an ambiguous or underspecified query, search result diversification aims at accommodating different user intents within a single Search Engine Result Page (SERP). While automatic identification of different intents for a given query is a crucial step for result diversification, also important is the estimation of intent types (informational vs. navigational). If it is possible to distinguish between informational and navigational intents, search engines can aim to return one best URL for each navigational intent, while allocating more space to the informational intents within the SERP. In light of the observations, we propose a new framework for search result diversification that is intent importance-aware and type-aware. Our experiments using the NTCIR-9 INTENT Japanese Subtopic Mining and Document Ranking test collections show that: (a) our intent type estimation method for Japanese achieves 64.4% accuracy; and (b) our proposed diversification method achieves 0.6373 in D#-nDCG and 0.5898 in DIN#-nDCG over 56 topics, which are statistically significant gains over the top performers of the NTCIR-9 INTENT Japanese Document Ranking runs. Moreover, our relevance oriented model significantly outperforms our diversity oriented model and the original model by Dou et al..

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 9th Asia Information Retrieval Societies Conference, AIRS 2013, Proceedings
Number of pages13
Publication statusPublished - 2013
Event9th Asia Information Retrieval Societies Conference on Information Retrieval Technology, AIRS 2013 - Singapore, Singapore
Duration: 2013 Dec 92013 Dec 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8281 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Asia Information Retrieval Societies Conference on Information Retrieval Technology, AIRS 2013


  • Intent Type
  • Search Result Diversity
  • Subtopic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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