pmse presentation

Personalized Mobile Search Engine

Project Coordinator: Miss. BHAVYASRI ASST.PROFESSOR

Presented by: K.AJAY[10G21A0552] G.MADANAMOHAN REDDY[10G21A0533] CH.SAI CHARAN[10G21A0517]

D.MAHESH BABU[10G21A0520]

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Content

1. Abstract2. Introduction3. Existing system and disadvantages4. Proposed System and advantages5. System architecture6. UML Diagrams7. Modules and Description8. System Configurations9. Screen Shots10. Conclusion and Future Works


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1.Abstract• PMSE captures the users preferences in the form of concepts by

mining their click through data.• Classification of information -Content concept -Location concept• User’s 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.


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2.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 user’s 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.


3.Existing system• Existing system requires users to manually enter

the location preferences

• Manually server retrieves a set of locations

• User profile being exposed

Disadvantages• Interactions are limited.

• User queries are shorter , more ambiguous

• No privacy preservation

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5.Proposed System• Most existing location-based search systems require users to

manually define their location preferences . 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.


Advantages• Preserving privacy issue

• Avoids overlooking of unnecessary document

• Train the user profiles quickly and efficiently

• Maintaining good ranking quality. • Improves retrieval effectiveness

Personalized Mobile Search Engine 9

5.System architecture

Fig. 1. The general process flow of PMSE.


ARCHITECTURE DESCRIPTION

First, computation-intensive tasks, such as RSVM training, should be handled by the PMSE server due to the limited computational power on mobile devices.

Second, data transmission between client and server should be minimized to ensure fast and efficient processing of the search.

Third, click through data representing precise user preferences on the search results, should be stored on the PMSE clients in order to preserve user privacy.

In the PMSE’s client-server architecture, PMSE clients are responsible for the storing user clickthroughs and the ontologies derived from the PMSE server.

Simple tasks, such as updating click thoughs and ontologies, creating feature vectors, and displaying re ranked search results are handled by the PMSE clients with limited computational power.

On the other hand, heavy tasks, such as RSVM training and reranking of search results, are handled by the PMSE server.


6.UML DIAGRAMS

1.CLASS DIAGRAM OF USER INTERACTION WITH SEARCH ENGINE


2.USE CASE DIAGRAM FOR CONCEPT BASED USER PROFILING


3.SEQUENCE DIAGRAM FOR CONCEPT BASED USER PROFILING


4.ACTIVITY DIAGRAM FOR CONCEPT BASED USER PROFILING


5.DATA FLOW DIAGRAM TO OBTAIN RERANKED RESULTS BASED ON USERS ONTOLOGY


7.MODULES AND DESCRIPTION

This system is implemented by dividing it as following four modules

MODULES INVOLVED :

1.Mobile Client

2.PMSE Server

3.Re-Rank Search Results

4.Ontology update and Click through collection


MODULE DESCRIPTION1. Mobile Client:

1.In the PMSE’s client-server architecture, PMSE clients are responsible for storing the user clickthroughs and the ontologies derived from the PMSE server.

2.Simple tasks, such as updating clickthroughs and ontologies, creating feature vectors, and displaying re-ranked search results are handled by the PMSE clients with limited computational power.

3.Moreover, in order to minimize the data transmission between client and server, the PMSE client would only need to submit a query together with the feature vectors to the PMSE server, and the server would automatically return a set of re-ranked search results according to the preferences stated in the feature vectors.


4.The data transmission cost is minimized, because only the essential data (i.e., query, feature vectors, ontologies and search results) are transmitted between client and server during the personalization process.


2.PMSE Server :

Heavy tasks, such as RSVM training and re-ranking of search results, are handled by the PMSE server. PMSE Server’s design addressed the issues:

(1)limited computational power on mobile devices, and

(2) data transmission minimization.

PMSE consists of two major activities:

1) Re-ranking the search results at the PMSE server, and

2) Ontology update and clickthroughs collection at a mobile client


3.Re-ranking the search results:

1.When a user submits a query on the PMSE client, the query together with the feature vectors containing the user’s content and location preferences (i.e., filtered ontologies according to the user’s privacy setting) are forwarded to the PMSE server, which in turn obtains the search results from the backend search engine (i.e., Google).

2.The content and location concepts are extracted from the search results and organized into ontologies to capture the relationships between the concepts. The server is used to perform ontology extraction for its speed.

3.The feature vectors from the client are then used in RSVM training to obtain a content weight vector and a location weight vector, representing the user interests based on the user’s content and location preferences for the re-ranking.


4.Again, the training process is performed on the server for its speed. The search results are then re-ranked according to the weight vectors obtained from the RSVM training.

5.Finally, the re-ranked results and the extracted ontologies for the personalization of future queries are returned to the client.


4.Ontology update and Click through collection:

1.The ontologies returned from the PMSE server contain the concept space that models the relationships between the concepts extracted from the search results.

2.They are stored in the ontology database on the client1. When the user clicks on a search result, the clickthrough data together with the associated content and location concepts are stored in the clickthrough database on the client.

3.The clickthroughs are stored on the PMSE clients, so the PMSE server does not know the exact set of documents that the user has clicked on.

4.This design allows user privacy to be preserved in certain degree. If the user is concerned with his/her own privacy, the privacy level can be set to high so that only limited personal information will be included in the feature vectors and passed along to the PMSE server for the personalization.


5.On the other hand, if a user wants more accurate results according to his/her preferences; the privacy level can be set to low so that the PMSE server can use the full feature vectors to maximize the personalization effect

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Block Diagram



8.SYSTEM CONFIGURATIONHARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz.

Hard Disk : 40 GB.

Floppy Drive : 1.44 Mb.

Monitor : 15 VGA Colour.

Mouse : Logitech.

Ram : 512 Mb.

MOBILE : ANDROID


SOFTWARE REQUIREMENTS:

Operating system : Windows XP.

Coding Language : Java 1.7

Tool Kit : Android 2.3 IDE : Eclipse


9.SCREEN SHOTS

1.STARTING THE EMULATOR


2.REPRESENTING SEARCH INTERFACE


3.REPRESENTS READING SEARCH RESULTS OF USER QUER FROM SERVER


4.ARRANGEMENTS OF SEARCH RESULTS BASED ON USER PREFERENCES


5.REPRESENTING THE LINKS TO THE USER QUERY AND USER CLICKING WIKIPEDIA


6.REPRESENTS THE RERANKED SEARCH RESULTS SUGGESTING WIKIPEDIA LINK AS FIRST RESULT

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10.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.


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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.


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