Personalized mobile search engine

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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.

Published in: Education, Technology, Design
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  • i want ieee papers or code
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  • @Saurav can u provide me a code and paper of this project coz i have the code of this project but it does not work properly... plz help mi
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  • can you explain the prototype of PMSE?
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  • i have the paper what u have mentioned first in refrncs
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  • no saurav i have papers but i want to know more about the paper
    how the proposed system works
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Personalized mobile search engine

  1. 1. Personalized Mobile Search Engine Project Coordinator: Prof. Anitha Dixit Prof. Pushpalatha S Nikkam Presented by: Saurav ########
  2. 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. 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. 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. Personalized Mobile Search Engine 49/18/2013
  5. 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. Personalized Mobile Search Engine 59/18/2013
  6. 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. Personalized Mobile Search Engine 69/18/2013
  7. 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. Personalized Mobile Search Engine 79/18/2013
  8. 8. Block Diagram Personalized Mobile Search Engine 89/18/2013
  9. 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. 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. 11. 9/18/2013Personalized Mobile Search Engine 11

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