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Adaptation of the Concept Hierarchy Model with Search Logs for Query Recommendation on Intranets. Date: 2012/12/13 Author: Ibrahim Adeyanju , Dawei Song , M- Dyaa Albakour , Udo Kruschwitz , Anne De Roeck , Maria Fasli Source: SIGIR " 12 Advisor: Jia -ling, Koh
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Adaptation of the Concept Hierarchy Model with Search Logs for Query Recommendation on Intranets
Author: Ibrahim Adeyanju, Dawei Song , M-DyaaAlbakour,
UdoKruschwitz , Anne De Roeck , Maria Fasli
Source: SIGIR "12
Advisor: Jia-ling, Koh
Speaker: JiunJia, Chiou
from the collection
discover relationships between terms based on statistical analysis
Give too many unsuitable suggestions for current queries that have a very high document frequency.
from the search logs
provide a conceptual viewof thedomain based on collective user intelligence
Not be able to give any suggestions for new queries that were never used in the past.
SHReC (SubsumptionHierarchy for ResultClustering)
α : the strength of subsumption
df(x) > df(y)
df(x ^ y) / df(y) ≥ α
A 、 B 、 C
G 、 H
Normalisation of weights w for each edge in the initial SHReCbuilt from the document collection only
A->D : =0.28
A->E : =0.45
A->F : =0.27
Basketball game nba
A --> D:
A --> E:
A --> F:
A --> P:
A --> Q:
Built from only the Intranet document collection and is not updated in any form throughout the test period.
Query Flow Graph
Built continually at periodic intervals from the search logs.
Ranking only the immediate nodes coming out of a query node rather than all nodes in the graph.
The document collections were created by crawling the main websites of the institutions with Nutch.
Obtain the document frequency of any term and co-occurrence document frequency of any two terms required for SHReC creation.
MRR = = 0.61
q : queryr :recommendation
ri: the rank of document clicked within the result
list displayed to the user
= 0.5-0.34 = 0.16
MRRC(q) = (++)/3 = 0.34
MRRC(r) = (1++)/3 = 0.5
= 0.21-0.61 = -0.4
MRRC(r) = (++)/3 = 0.21
MRRC(q) = (1++)/3 = 0.61
your attention !