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Service Rating Prediction by Exploring Social Mobile Users’
Geographical Locations
Abstract—Recently, advances in intelligent mobile device and positioning techniques have
fundamentally enhanced social networks, which allows users to share their experiences, reviews,
ratings, photos, check-ins, etc. The geographical information located by smart phone bridges the
gap between physical and digital worlds. Location data functions as the connection between
user’s physical behaviors and virtual social networks structured by the smart phone or web
services. We refer to these social networks involving geographical information as location-based
social networks (LBSNs). Such information brings opportunities and challenges for
recommender systems to solve the cold start, sparsity problem of datasets and rating prediction.
In this paper, we make full use of the mobile users’ location sensitive characteristics to carry out
rating predication. We mine: 1) the relevance between user’s ratings and user-item geographical
location distances, called as user-item geographical connection, 2) the relevance between users’
rating differences and user-user geographical location distances, called as user-user geographical
connection. It is discovered that humans’ rating behaviors are affected by geographical location
significantly. Moreover, three factors: user-item geographical connection, user-user geographical
connection, and interpersonal interest similarity, are fused into a unified rating prediction model.
We conduct a series of experiments on a real social rating network dataset Yelp. Experimental
results demonstrate that the proposed approach outperforms existing models.
Existing System:
The first generation of recommender systems with traditional collaborative filtering algorithms
is facing great challenges of cold start for users (new users in the recommender system with little
historical records) and the sparsity of datasets. Fortunately, with the popularity and rapid
development of social networks, more and more users enjoy sharing their experiences, reviews,
ratings, photos, and moods with their friends. Many social- based models [10]-[16], [62] have
been proposed to improve the performance of recommender system. Like some existing system
proposed to use the concept of ‘inferred trust circle’ based on the domain-obvious of circles of
friends on social networks to recommend users favorite items.
Disadvantages:
 Cold Start problem for new users.
 Limitations are there for combine rating and recommendation.
Proposed System
In this paper, we make full use of the mobile users’ location sensitive characteristics to carry out
rating predication. We propose, 1) to mine the relevance between user’s ratings and useritem
geographical location distances, called as user-item geographical connection, 2) to mine the
relevance between users’ rating differences and user-user geographical location distances, called
as user-user geographical connection, and 3) to find the people whose interest is similar to users.
In this paper, three factors are taken into consideration for rating prediction: user-item
geographical connection, user-user geographical connection, and interpersonal interest similarity.
These factors are fused into a location based rating prediction model. The novelties of this paper
are user-item and user-user geographical connections, i.e. we explore users’ rating behaviors
through their geographical location distances.
Advantages:
 We mine the relevance between ratings and useritem geographical location distances.
 We mine the relevance between users’ rating differences.
 We integrate three factors: user-item geographical connection, user-user geographical
connection, and interpersonal interest similarity
SYSTEM REQUIREMENTS
HARDWARE REQUIREMENTS:
Hardware : Pentium
Speed : 1.1 GHz
RAM : 1GB
Hard Disk : 20 GB
SOFTWARE REQUIREMENTS:
Operating System : Windows Family
Technology : Java and J2EE
Web Technologies : Html, JavaScript, CSS
Web Server : Apache Tomcat 7.0/8.0
Database : My SQL 5.5 or Higher
UML's : StarUml
Java Version : JDK 1.7 or 1.8
Implemented by
Development team : Cloud Technologies
Website : http://www.cloudstechnologies.in/
Contact : 8121953811, 040-65511811

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Service rating prediction by exploring social mobile users’ geographical locations

  • 1. Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations Abstract—Recently, advances in intelligent mobile device and positioning techniques have fundamentally enhanced social networks, which allows users to share their experiences, reviews, ratings, photos, check-ins, etc. The geographical information located by smart phone bridges the gap between physical and digital worlds. Location data functions as the connection between user’s physical behaviors and virtual social networks structured by the smart phone or web services. We refer to these social networks involving geographical information as location-based social networks (LBSNs). Such information brings opportunities and challenges for recommender systems to solve the cold start, sparsity problem of datasets and rating prediction. In this paper, we make full use of the mobile users’ location sensitive characteristics to carry out rating predication. We mine: 1) the relevance between user’s ratings and user-item geographical location distances, called as user-item geographical connection, 2) the relevance between users’ rating differences and user-user geographical location distances, called as user-user geographical connection. It is discovered that humans’ rating behaviors are affected by geographical location significantly. Moreover, three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity, are fused into a unified rating prediction model. We conduct a series of experiments on a real social rating network dataset Yelp. Experimental results demonstrate that the proposed approach outperforms existing models. Existing System: The first generation of recommender systems with traditional collaborative filtering algorithms is facing great challenges of cold start for users (new users in the recommender system with little historical records) and the sparsity of datasets. Fortunately, with the popularity and rapid development of social networks, more and more users enjoy sharing their experiences, reviews, ratings, photos, and moods with their friends. Many social- based models [10]-[16], [62] have been proposed to improve the performance of recommender system. Like some existing system proposed to use the concept of ‘inferred trust circle’ based on the domain-obvious of circles of friends on social networks to recommend users favorite items.
  • 2. Disadvantages:  Cold Start problem for new users.  Limitations are there for combine rating and recommendation. Proposed System In this paper, we make full use of the mobile users’ location sensitive characteristics to carry out rating predication. We propose, 1) to mine the relevance between user’s ratings and useritem geographical location distances, called as user-item geographical connection, 2) to mine the relevance between users’ rating differences and user-user geographical location distances, called as user-user geographical connection, and 3) to find the people whose interest is similar to users. In this paper, three factors are taken into consideration for rating prediction: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity. These factors are fused into a location based rating prediction model. The novelties of this paper are user-item and user-user geographical connections, i.e. we explore users’ rating behaviors through their geographical location distances. Advantages:  We mine the relevance between ratings and useritem geographical location distances.  We mine the relevance between users’ rating differences.  We integrate three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS: Hardware : Pentium
  • 3. Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB SOFTWARE REQUIREMENTS: Operating System : Windows Family Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS Web Server : Apache Tomcat 7.0/8.0 Database : My SQL 5.5 or Higher UML's : StarUml Java Version : JDK 1.7 or 1.8 Implemented by Development team : Cloud Technologies Website : http://www.cloudstechnologies.in/ Contact : 8121953811, 040-65511811