Location Based Recommendation Application

For our senior design project we decided to make an iOS application that could generate a list of nearby locations for the user to visit. We wanted the recommendation list to be unique for each user instead of a list of the most popular locations in the area. To accomplish this we developed our own recommendation algorithm from scratch. The algorithm uses a tagging system in which users and system administrators are able to add and modify the tags associated with locations. By using the tags associated with each location and with each user, our algorithm is able to generate a recommendation list tailored to the interests of each user. We made an intuitive user interface by creating a simple, clean layout for each screen the user see. We wanted the users to be able to glance at the screen and immediately know what is possible to do while at that screen and what is the purpose of said screen. We have clearly labeled buttons for each of the possible actions, and use color coordination to draw the users attention to important information. By the end of the project we had an application with an intuitive user interface and an algorithm that could achieve our goal. We still need to link the application, server, and algorithm together, so that they act as a single system. One this is done the core project will be functional, and we can begin adding in additional features

Review of Recommendation on Location Based Services

In every Era “Location” is a strong component of “Mobility” Location based services (LBS) are services offered using mobile phone by taking mobile’s geographical location. The proposed system is providing location based services and offers with respect to user interest . Vendors are allowed to post and edit an advertisement for users. The system contains various modules such as advertising , Social program, Tourist place, Parking place, Emergency calls etc. The system uses apriori algorithm for mining frequent ratings from user. This information is used to provide popularity of location. It also provides user’s feedback, ranking based suggestion in secured manner. The purpose of this system is to notify the user based on their preferences and their interest in the particular area and notify them using android application. This will lead to lower advertising costs and expenditures also save the time of user for finding the located area of ads with help of GPS

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC 2015 systems

Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm

On Information Coverage for Location Category Based Point-of-Interest Recommendation

Point-of-interest(POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories(like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city.In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city.The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms