PMSE captures the users’ preferences in the form of concepts by mining their click through data. Classification of location information -Content concept -Location concept Users’ locations (positioned by GPS) are also used. The user preferences are organized in an ontology-based, multi-facet user profile. The client- collects and stores locally clickthrough data to protect privacy. At server- concept extraction, training and reranking. Privacy issue – is taken care by restricting the information in the user profile. We prototype PMSE on the Google Android platform. Results show that PMSE significantly improves the precision comparing to the baseline.

1. Personalized Mobile
Search Engine
Project Coordinator:
Prof. Anitha Dixit
Prof. Pushpalatha S Nikkam
Presented by:
Saurav ########

2. Content
1. Abstract
2. Introduction
3. Literature Survey
4. Differences Existing System And Proposed System
5. Block Diagram
6. Conclusions And Future Work
7. References
Personalized Mobile Search Engine 29/18/2013

3. Abstract
• PMSE captures the users’ preferences in the form of concepts by mining
their click through data.
• Classification of location information
-Content concept
-Location concept
• Users’ locations (positioned by GPS) are also used.
• The user preferences are organized in an ontology-based, multi-
facet user profile.
• The client- collects and stores locally clickthrough data to protect
privacy.
• At server- concept extraction, training and reranking.
• Privacy issue – is taken care by restricting the information in the user
profile.
• We prototype PMSE on the Google Android platform.
• Results show that PMSE significantly improves the precision comparing to
the baseline.
Personalized Mobile Search Engine 39/18/2013

4. Introduction
• Problem - Interactions between the users and search engines are limited.
• As a result, mobile users tend to submit shorter, hence, more ambiguous
queries.
• In order to return highly relevant results ,mobile search engines must be
able to profile the users’ interests and personalize the results accordingly.
• A practical approach to carry out this is to analyse the user’s clickthrough
data .
• However, most of the previous work assumed that all concepts are of the
same type.
• We separate concepts into location concepts and content concepts to
recognize information importance.
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5. Literature Survey
• So far there have been many papers written & researched on search
engines. There is tremendous evolvement in this field.
• But there is only one such paper written so far on Personalised Mobile
Search Engine [PMSE].
• PMSE: A Personalized Mobile Search Engine
Author: Kenneth Wai-Ting Leung, Dik Lun Lee, and Wang-Chien Lee
Published: April 2013, vol 25
• In this paper, we propose a realistic design for PMSE by adopting the
metasearch approach which replies on one of the commercial search
engines, such as Google, Yahoo, or Bing, to perform an actual search.
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6. (contd.,)
• Studies the unique characteristics of content and location concepts, and
provides a coherent strategy using a client-server architecture to
integrate them into a uniform solution for the mobile environment.
• By mining content and location concepts for user profiling, it utilizes both
the content and location preferences to personalize search results for a
user.
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7. The differences between
existing works and ours are
• Most existing location-based search systems require users to manually
define their location preferences or to manually prepare a set of
location sensitive topics. PMSE profiles both of the user’s content and
location preferences in the ontology based user profiles, which are
automatically learned from the clickthrough and GPS data without
requiring extra efforts from the user.
• We propose and implement a new and realistic design for PMSE. To train
the user profiles quickly and efficiently.
• Existing works on personalization do not address the issues of privacy
preservation. PMSE addresses this issue by controlling the amount of
information in the client’s user profile being exposed to the PMSE server
using two privacy parameters, which can control privacy smoothly, while
maintaining good ranking quality.
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8. Block Diagram
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9. Conclusions and Future work
• The proposed personalized mobile search engine is an innovative
approach for personalizing web search results. By mining content and
location concepts for user profiling, it utilizes both the content and
location preferences to personalize search results for a user.
• The results show that GPS location helps improve retrieval effectiveness
for location queries (i.e., queries that retrieve lots of location information).
• For future work, we will investigate methods to exploit regular travel
patterns and query patterns from the GPS and clickthrough data to
further enhance the personalization effectiveness of PMSE.
Personalized Mobile Search Engine 99/18/2013

10. References
[1] Kenneth Wai-Ting Leung, Dik Lun Lee, and Wang-Chien Lee “PMSE: A
Personalized Mobile Search Engine” – IEEE TRANSACTIONS ON KNOWLEDGE
AND DATA ENGINEERING, VOL. 25, NO. 4, APRIL 2013.
[2] "Google Personalized Results Could Be Bad for Search". Network World.
Retrieved July 12, 2010.
[3] "Search Engines and Customized Results Based on Your Internet History".
SEO Optimizers. Retrieved 27 February 2013.
[4] E. Agichtein, E. Brill, and S. Dumais, “Improving web search ranking by
incorporating user behavior information,” in Proc. of ACM SIGIR
Conference, 2006.
Personalized Mobile Search Engine 109/18/2013

11. 9/18/2013Personalized Mobile Search Engine 11