Transcript Zhang Tao

UOS
Ontology Based Personalized Search
Zhang Tao
The University of Seoul
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Zhang Tao
Contents
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Overview
Determining the content of
documents
User Profiles
Improving Search Results
Conclusions and Future Work
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Overview
Proposing a problem
With the exponentially growing amount of information
available on the Internet, the task of retrieving documents
of interest has become increasingly difficult.
People have two ways to find the data they are looking
for: search and browse
In terms of searching, about one half of all retrieved
documents have been reported to be irrelevant. Why?
Conclusion: How is the effective personalization system?
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Overview
The study of this paper
This paper studies ways to model a user’s interests and
shows how these profiles can be deployed for more
effective information retrieval and filtering.
A user profile is created over time by analyzing surfed
pages.
This paper shows how the profiles can be used to
achieve search performance improvements.
 Introduce the OBIWAN project
The goal of OBIWAN is to investigate a novel contentbased approach to distributed information retrieval.
Websites are clustered into regions.
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Overview
The architecture is a hierarchy of regions.
The text classifier is a core component not only of the
entire OBIWAN project, but also of the presented
personalization method.
 Related Work
Personalization is a broad field of very active ongoing
research.
Applications include personalized access to certain
resources and filtering/rating systems.
SmartPush is currently the only system to store profiles
as concept hierarchies.
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Determining the content of documents
Importance
User interests are inferred by analyzing the web pages
the user visits.
For this purpose, it is necessary to determine the content,
or characterize of these surfed pages.
 A hierarchy of concepts
This ontology is based on a publicly accessible browsing
hierarchy.
Each node is associated with a set of documents, all of
documents for node are merged into a superdocument.
Documents as well as superdocuments are represented
as weighted keyword vectors
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Determining the content of documents
This page vector is compared with the keyword vectors
associated with every node to calculate similarities.
The nodes with the top matching vectors are assumed to
be most related to the content of the surfed page.
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User Profiles
Introduce
User profiles store approximations of the interests of a
given user.
User profiles include three features:
• hierarchically structured, and not just a list of keywords
• generated automatically, without explicit user feedback
• Dynamical
 Creation and Maintenance
Profiles are generated by analyzing the surfing behavior
of a user. “Surfing behavior” here refers to the length of
the visited pages and the time spent thereon.
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User Profiles
Four different combinations of time, length, and subject
discriminators have been investigated.
In the following function, time refers to the time a user
spent on a given page, and length refers to the length of
the page, ɤ(d,ci) is the strength of the match between the
content of document d and category ci. △L(ci) represents
the interest L in a category ci.
time
 r (d , Ci)
log length
(1)
time
 r (d , Ci)
log log length
(2)
L(Ci)  log
L(Ci)  log
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User Profiles
Profile Evaluation: Convergence
The evaluation of the user profiles consists of two parts:
• A notion of convergence is introduced with respect to which 16
actual user profiles are discussed.
• Examines the relationship between the calculated user interests
and the actual user interests.
Figure 1 shows a sample profile (adjustment function 2),
it consists of roughly 75 non-zero categories.
Figure 2 shows the numbers of non-zero categories for
five sample profiles with 100-150 categories created
using the same interest adjustment function.
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User Profiles
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User Profiles
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User Profiles
On average, that corresponds to roughly 320 pages, or
17 days of surfing. Table 1 summarizes the convergence
properties.
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User Profiles
Comparison with actual user interests
Although convergence is a desirable property, it does not
measure the accuracy of the generated profiles.
The sixteen users were shown the top twenty subjects in
their profiles in random order and asked how
appropriately these inferred categories reflected their
interests.
Table 2 shows the experiment for the answers to some
questions with the top 20 and top 10 categories
respectively.
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User Profiles
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Improving Search Results
A problem about search results
The wealth of information available on the web is actually
too large.
As to search results, the top ranked documents a user
can have a look at are often not relevant to this user.
There are three common approaches to address this
problem:
• Re-ranking: The algorithms apply a function to the ranking
numbers that have been returned by the search engine.
• Filtering: Filtering systems determine which documents in the
results sets are relevant and which are not.
• Query Expansion: If a query can be expanded with the user’s
interests, the search results are likely to be more narrowly
focused.
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Improving Search Results
Re-Ranking
Given a query, re-ranking is done by modifying the
ranking that was returned by a publicly accessible search
engine.
ProFusion (www.profusion.com) in this case. The idea is
to characterize each of the returned documents and, by
referring to the user profiles, to determine how much a
user is interested in these categories.
The following function is the adjustment function of the
Re-ranking method.
1 4
Q( Dj )  w( Dj )  (0.5    (Ci )  r ( Dj, Ci ))
4 i 1
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Improving Search Results
Evaluation
The results that have been produced by the different reranking systems must be evaluated.
The eleven point precision average is the better measure
method.
The eleven point precision average evaluates ranking
performance in terms of recall and precision.
Number of relevant items retrieved
Recall =
Number of relevant items in collection
Number of relevant items retrieved
Precision =
Total number of items retrieved
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Improving Search Results
Figure 3 shows the recall-precision graphs for one
interest adjustment functions.
Figure 4 shows The remaining set of 16 queries were
evaluated using this function.
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Improving Search Results
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Improving Search Results
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Improving Search Results
Filtering
To filter a set of result documents means to exclude some
documents.
Filtering was done by using the above ranking functions
with thresholds to decide which documents were
irrelevant and which were not.
Figures 5 and 6 show the performance of the filter for the
training and the testing set, respectively.
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Improving Search Results
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Conclusion and Future Work
Conclusion
These profiles have been shown to converge and to
reflect actual user interests quite well.
With the presented approach, the length of a surfed page
can be neglected when the interest in a page is inferred.
Future work
Future work includes the integration of the system into a
web browser.
Other areas of profile deployment are conceivable.
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