Estimating Intent Types for Search Result Diversification

Kosetsu Tsukuda, Tetsuya Sakai, Zhicheng Dou, and Katsumi Tanaka

Abstract

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..

Details

Publication typeInproceedings
Published inProceedings of AIRS 2013
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