# An Empirical Recommendation Framework to Support Location-Based Services +
## 1 Introduction  
Modern technology has made our day to day life easier and more comfortable. We can see the blessing of modern technology in every aspect of our life. People today follow a trend of slacking off their work. Localizing their position can specify one's location that helps us to know where they are. In the World Wide Web (www), Information Retrieval (IR) plays an important role to provide resources that can be further used in research purposes. IR extracts the right information which can satisfy user requirements for any kind of data analysis[1].  
Location-based service (LBS) is a commonly used term in the IR system. It generally uses the geographical data of a user's surrounding and provide services. Recommendation System (RS) is oneof the key features of LBS. RS takes information from a user and analyses it to find the most relevant items according to the user's choices [2]. It becomes a common application that provides suggestions based on the user's interest. Mobile devices with GPS technology provide a vast range of information about user's activities [3]. This information works as a data source to improve the quality of the modern RS.  
There has been a lot of work on the RS. Most of them used to find similar users and make suggestions. Thus, it sometimes fails to give results according to the user's choice. In this era of modern technology, it is necessary to find an effective and efficient RS system. The parameter of this system may include information about travel destinations, entertainment, activities and different locations, etc. based on the user's interest [4]. After considering this information RS provide a recommendation to the user. A recommendation can also provide a suitable place, where a group of friends can meet together according to their preferred type of places and previously tagged in places. As RS considers the user profile, it calculates the similarity between different profiles and suggests the best matching. However, finding a suitable place that users might be interested in or meeting together by a group of users using their personal preferences is not that easy. Therefore, challenges appear when a group of friends wants to meet in a place. To build a RS we have to consider the following points:  
* Firstly we make a user interest profile for each user according to their previous activity.
* Then we create a map presentation using the grid structure for all POIs so that we can reduce the processing complexity.
* Finally we calculate the similarity between user profile and POIs to recommend a user as well as a group of user. We also measure the Manhattan distance between the user location and POIs to provide the shortest path.  
In this paper, our main objective is to identify the user's interest and provide recommendations about places. These suggestions are of two categories: one is for a single user and the other is to suggest a suitable place for a group of users measuring their similarities. All the suggested places have a minimum distance from the user's current location. We have adapted a grid-based structure for our system to reduce computational time and also collaborative filtering (CF) method to find similar users with similar activities.