A New Algorithm for Inferring User
Search Goals with Feedback Sessions
ABSTRACT:
For a broad-topic and ambiguous query, different
users may have different search goals when they submit it to a search engine.
The inference and analysis of user search goals can be very useful in improving
search engine relevance and user experience. In this paper, we propose a novel
approach to infer user search goals by analyzing search engine query logs.
First, we propose a framework to discover different user search goals for a
query by clustering the proposed feedback sessions. Feedback sessions are
constructed from user click-through logs and can efficiently reflect the
information needs of users. Second, we propose a novel approach to generate
pseudo-documents to better represent the feedback sessions for clustering.
Finally, we propose a new criterion “Classified Average Precision (CAP)” to
evaluate the performance of inferring user search goals. Experimental results
are presented using user click-through logs from a commercial search engine to
validate the effectiveness of our proposed methods.
EXISTING SYSTEM:
We define user search goals as the
information on different aspects of a query that user groups want to obtain.
Information need is a user’s particular desire to obtain information to satisfy
his/her need. User search goals can be considered as the clusters of
information needs for a query. The inference and analysis of user search goals
can have a lot of advantages in improving search engine relevance and user
experience.
DISADVANTAGES
OF EXISTING SYSTEM:
·
What users care about varies a lot for
different queries, finding suitable predefined search goal classes is very
difficult and impractical.
·
Analyzing the clicked URLs directly from
user click-through logs to organize search results. However, this method has
limitations since the number of different clicked URLs of a query may be small.
Since user feedback is not considered, many noisy search results that are not
clicked by any users may be analyzed as well. Therefore, this kind of methods
cannot infer user search goals precisely.
·
Only identifies whether a pair of
queries belongs to the same goal or mission and does not care what the goal is
in detail.
PROPOSED SYSTEM:
In this paper, we aim at discovering the
number of diverse user search goals for a query and depicting each goal with
some keywords automatically. We first propose a novel approach to infer user
search goals for a query by clustering our proposed feedback sessions. Then, we
propose a novel optimization method to map feedback sessions to
pseudo-documents which can efficiently reflect user information needs. At last,
we cluster these pseudo documents to infer user search goals and depict them
with some keywords.
ADVANTAGES
OF PROPOSED SYSTEM:
To sum up, our work has three major
contributions as follows:
·
We propose a framework to infer
different user search goals for a query by clustering feedback sessions. We
demonstrate that clustering feedback sessions is more efficient than clustering
search results or clicked URLs directly. Moreover, the distributions of
different user search goals can be obtained conveniently after feedback
sessions are clustered.
·
We propose a novel optimization method
to combine the enriched URLs in a feedback session to form a pseudo-document,
which can effectively reflect the information need of a user. Thus, we can tell
what the user search goals are in detail.
·
We propose a new criterion CAP to
evaluate the performance of user search goal inference based on restructuring
web search results. Thus, we can determine the number of user search goals for
a query.
SYSTEM ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA
ü Java Version :
JDK 1.6 & above.
REFERENCE:
Zheng Lu, Student Member, IEEE, Hongyuan
Zha, Xiaokang Yang, Senior Member, IEEE, Weiyao Lin, Member, IEEE, and Zhaohui
Zheng –“A New Algorithm for Inferring User Search Goals with Feedback Sessions”-
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING, VOL. 25, NO. 3, MARCH 2013.