Wednesday, 23 August 2017

IEEE 2017 B-Tech Main & M-Tech Project | Point-of-interest Recommendation for Location Promotion in Location-based Social Networks


Point-of-interest Recommendation for Location Promotion in Location-based Social Networks


Abstract

With the wide application of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become one of the major services in LBSNs. The behaviors of users in LBSNs are mainly checking in POIs, and these checking in behaviors are influenced by user’s behavior habits and his/her friends. In social networks, social influence is often used to help businesses to attract more users. Each target user has a different influence on different POI in social networks. This paper selects the list of POIs with the greatest influence for recommending users. Our goals are to satisfy the target user’s service need, and simultaneously to promote businesses’ locations (POIs). This paper defines a POI recommendation problem for location promotion. Additionally, we use sub modular properties to solve the optimization problem. At last, this paper conducted a comprehensive performance evaluation for our method using two real LBSN datasets. Experimental results show that our proposed method achieves significantly superior POI recommendations comparing with other state-of-the-art recommendation approaches in terms of location promotion.


Existing System

Recently, many researchers have been engaged in location-aware services. In LBSNs, users can post comments on locations or activities, upload photos, and share check-in locations in which users are interested with friends. These locations are called points-of-interest (POIs). Currently, POI recommendation has become one of main location-aware services in LBSNs. POI recommendation approaches mostly involve recommending users with some locations in which users may be interested based on users’ characters, preferences, and behavioral habits. Through the detailed analysis above, we observe traditional POI recommendations rarely focus on the effect of social relationships for businesses location promotion through the POI recommendation process.


Disadvantages: • No Concept for on the location promotion in LBSNs. • Helps only for business people not for users.

Proposed System

In view of POIs, POIs (e.g.restaurants, hotel, markets) have to explore checking-in records to attract more users to visit; more users (e.g., friends of users that checked in these POIs) will be influenced to check in these locations. In this paper, we regard the influence on the business as a maximization location promotion problem. The essential goals of recommendation system are to satisfy users’ service demands and merchants’ advertising needs. this paper proposes POI recommendation method for promoting POIs. Our proposed method is not only a tool for businesses to use to promote their products and attract more customers to visit their stores, but also recommends users with some POI’s satisfying users preferences.


Advantages: • We propose a novel point-of-interest recommendation problem, and its goal is to promote the businesses’ locations ( POIs ). • We define the user’s IS under special POI categories in an entire social network, and model user mobility to describe the geographical influence between user. Few Points:

In this paper, focus on POI recommendation to social user based on his friends and friends of friends instead of unknown recommendation. Main Application collects check in data with geo properties. Like user from his location move to POI , PGu,v(l) tradeoff between geographical influence. And Target user to another user relation, PTu,v(l) semantic influence b/w u and v. POI recommendation approaches mostly involve recommending users with some locations in which users may be interested based on users’ characters, preferences. Like Facebook no suggest you some business locations according to your interests.


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 :

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