Project Guide:- Miss. Jayashree Patade Head Of Department:- Mrs. Geeta Atkar

Prepared By:- Mr. Dawange Santosh Kailas.(46) Mr. Ghadge Akshay Sanjay.(47) Mr. Gire Sanket Ramesh.(48)

Mr. Talekar Vikas Dilip.(49)

PMSE: Personalized Mobile Search Engine

Introduction

Proposed System

Existing System

Modules

Mobile Client

PMSE Server

Re-Rank Search Results

Ontology update and Click-through collection

Conclusion

Contents :-

We propose a personalized mobile search engine (PMSE) that

captures the users’ preferences in the form of concepts by

mining their click through data.

Due to the importance of location information in mobile

search, PMSE classifies these concepts into content concepts

and location concepts.

In addition, users’ locations (positioned by GPS) are used to

supplement the location concepts in PMSE.

Introduction :-

The user preferences are organized in an ontology-based, multifaceted user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results.

We prototype PMSE on the Google Android platform. Experimental results show that PMSE significantly improves the precision comparing to the baseline.

Most of the previous work assumed that all concepts are of the same type.

Existing System:-

Observing the need for different types of concepts, In particular, recognizing the importance of location information in mobile search, we separate concepts into location concepts and content concepts.

Proposed System:-

Mobile Client

PMSE Server

Re-Rank Search Results

Ontology update and Click-through collection

PMSE Modules:-

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

Mobile Client:-

Heavy tasks, such as RSVM training and re-ranking of search results, are handled by the PMSE server.

PMSE Server:-

The search results are then re-ranked according to the weight vectors obtained from the RSVM training.

Re-Ranking the Search Results:-

When the user clicks on a search result, the click-through data together with the associated content and location concepts are stored in the click-through database on the client.

The click-through are stored on the PMSE clients, so the PMSE server does not know the exact set of documents that the user has clicked on. This design allows user privacy to be preserved in certain degree.

Click-through Collection:-

The average time taken to fetch standard search results, re-rank & display them is less than 2 seconds, which is acceptable & almost real-time on a mobile device.

We also proposed two privacy parameters, min-Distance and exp-Ratio, to address privacy issues in PMSE by allowing users to control the amount of personal information exposed to the PMSE server.

Conclusion :-

THANK YOU!!!