26-08-2014, 10:07 AM
PMSE:
[attachment=67020]
OBJECTIVE:
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.
SYNOPSIS:
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. The user preferences are organized in an ontology-based, multi facet
user profile, which are used to adapt a personalized ranking function for rank adaptation of future
search results. To characterize the diversity of the concepts associated with a query and their
relevance’s to the user’s need, four entropies are introduced to balance the weights between the
content and location facets. Based on the client-server model, we also present a detailed
architecture and design for implementation of PMSE. In our design, the client collects and stores
locally the click through data to protect privacy, whereas heavy tasks such as concept extraction,
training, and re ranking are performed at the PMSE server. Moreover, we address the privacy
issue by restricting the information in the user profile exposed to the PMSE server with two
privacy parameters. We prototype PMSE on the Google Android platform. Experimental results
show that PMSE significantly improves the precision comparing to the baseline.
LIMITATIONS:
The number of users and queries in the experiments are small. This means that the results
from the experiments cannot be construed as representative in diverse situations.
Since users are given with predefined queries and topical interests, they have to synthesize
their information needs from the given queries and topical interests and conduct their
searches correspondingly.
Thus, their search behaviours in the experiments may be quite different from what they might
have exhibited when they attempt to resolve real-life information needs.
PROPOSED SYSTEM
PMSE profiles both of the user’s content and location preferences in the ontology based user
profiles, which are automatically learned from the click through 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, our design forwards user requests to the PMSE server to handle the
training and re ranking processes.
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:
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.
It 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.
PMSE incorporates a user’s physical locations in the personalization process. We conduct
experiments to study the influence of a user’s GPS locations in personalization. The results
show that GPS locations helps improve retrieval effectiveness for location queries
HARDWARE SPECIFICATION:
1 GB RAM
80 GB Hard Disk
Intel Processor
Datacard for Static IP
Internet Connection
Android Mobile with GPS, GPRS
SOFTWARE SPECIFICATION:
Windows OS
JDK 1.7
Apache Tomcat 7
Eclipse with Andoid plugin
MySql server
[attachment=67020]
OBJECTIVE:
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.
SYNOPSIS:
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. The user preferences are organized in an ontology-based, multi facet
user profile, which are used to adapt a personalized ranking function for rank adaptation of future
search results. To characterize the diversity of the concepts associated with a query and their
relevance’s to the user’s need, four entropies are introduced to balance the weights between the
content and location facets. Based on the client-server model, we also present a detailed
architecture and design for implementation of PMSE. In our design, the client collects and stores
locally the click through data to protect privacy, whereas heavy tasks such as concept extraction,
training, and re ranking are performed at the PMSE server. Moreover, we address the privacy
issue by restricting the information in the user profile exposed to the PMSE server with two
privacy parameters. We prototype PMSE on the Google Android platform. Experimental results
show that PMSE significantly improves the precision comparing to the baseline.
LIMITATIONS:
The number of users and queries in the experiments are small. This means that the results
from the experiments cannot be construed as representative in diverse situations.
Since users are given with predefined queries and topical interests, they have to synthesize
their information needs from the given queries and topical interests and conduct their
searches correspondingly.
Thus, their search behaviours in the experiments may be quite different from what they might
have exhibited when they attempt to resolve real-life information needs.
PROPOSED SYSTEM
PMSE profiles both of the user’s content and location preferences in the ontology based user
profiles, which are automatically learned from the click through 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, our design forwards user requests to the PMSE server to handle the
training and re ranking processes.
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:
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.
It 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.
PMSE incorporates a user’s physical locations in the personalization process. We conduct
experiments to study the influence of a user’s GPS locations in personalization. The results
show that GPS locations helps improve retrieval effectiveness for location queries
HARDWARE SPECIFICATION:
1 GB RAM
80 GB Hard Disk
Intel Processor
Datacard for Static IP
Internet Connection
Android Mobile with GPS, GPRS
SOFTWARE SPECIFICATION:
Windows OS
JDK 1.7
Apache Tomcat 7
Eclipse with Andoid plugin
MySql server