Location-based Recommendation System Research Articles

Collaborative filtering by graph convolution network in location-based recommendation system

Collaborative filtering by graph convolution network in location-based recommendation system

Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

Location Based Recommendation System with machine learning.

Machine learning allows us to give vast volumes of data to computer algorithms and have them assess and make data-driven judgments and recommendations entirely based on the incoming data. This project will employ machine learning to analyze geolocation data and user preferences in order to provide the user with intelligent recommendations. In today's fast-paced and hectic world, it's common to be too exhausted to prepare a home-cooked supper. Even if you eat home-cooked meals every day, it's not uncommon to desire to treat yourself to a decent dinner now and then for social or recreational reasons. Consider the instance of someone who has recently relocated to a new location. They already have particular tastes and interests. . If a person lives near his favorite outlet, it will save him and his food providers a lot of time and effort. This project involves categorizing popular snack destinations for incoming students based on their mood and preferences for amenities, affordability, and proximity to the place using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choice) by using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choice) by using K-Means Clustering to find the easiest food location for students in Greater Noida.

Community detection of trajectory data for location-based facility recommendation system

Community detection of trajectory data for location-based facility recommendation system

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

A comparative study of location-based recommendation systems

AbstractRecent advancements in location-based recommendation system (LBRS) and the availability of online applications, such as Twitter, Instagram, Foursquare, Path, and Facebook have introduced new research challenges in the area of LBRS. Use of content, such as geo-tagged media, point location-based, and trajectory-based information help in connecting the gap between the online social networking services and the physical world. In this article, we present a systematic review of the scientific literature of LBRS and summarize the efforts and contributions proposed in the literature. We have performed a qualitative comparison of the existing techniques used in the area of LBRS. We present the basic filtration techniques used in LBRS followed by a discussion on the services and the location features the LBRS utilizes to perform the recommendations. The classification of criteria for recommendations and evaluation metrics are also presented. We have critically investigated the techniques proposed in the literature for LBRS and extracted the challenges and promising research topics for future work.

Location Recommendation on a Street Random Waypoint Mobility Model Based on Predictive Model

Mobile Ad-Hoc Network (MANET) is a collection of mobile nodes which form a temporary network through wireless devices - without requiring any existing infrastructure. In the real world, user's behaviour is depended on many major factors such as weather, situation, location, date and time. While mob ile nodes can move with diverse patterns, it is difficult to accurately predict the events henceforward. In this paper, we propose an intelligence location-based recommendation system to predict a new route destination prediction method based on the movement history. On the whole, the follo wing steps outline the methodology of our studies: First, we collect the mob ile user's movement history. We begin by scrutinizing the characteristics of movement patterns all the way through mobility trace files obtained fro m GPS. Second, we demonstrate how prediction can be made using the mobility model parameters to investigate the influence of other parameter variations using Bayes' theorem. Third, we propose a recommendation system for ad justing the weight function adaptively. Finally, through a series of experiments, our proposed method aims to deliver performance in terms of accuracy and applicability under various system conditions.

Location Based Recommendation for Mobile Users Using Language Model and Skyline Query

Location based personalized recommendation has been introduced for the purpose of providing a mobile user with interesting information by distinguishing his preference and location. In most cases, mobile user usually does not provide all attributes of his preference or query. In extreme case, especially when mobile user is moving, he even does not provide any preference or query. Meanwhile, the recommendation system database also does not contain all attributes that can express what the user needs. In this paper, we design an effective location based recommendation system to provide the most possible interesting places to a user when he is moving, according to his implicit preference and physical moving location without the users providing his preference or query explicitly. We proposed two circle concepts, physical position circle that represents spatial area around the user and virtual preference circle that is a non-spatial area related to users interests. Those skyline query places in physical position circle which also match mobile users implicit preference in virtual preference circle will be recommended. Users implicit preference will be estimated under language modeling framework according to users historical visiting behaviors. Experiments show that our method is effective in recommending interesting places to mobile users. The main contribution of the paper comes from the combination of using skyline query and information retrieval to do an implicit location-based personalized recommendation without users providing explicit preference or query.