Rethinking User Generated Location Rating: Where Does the Lion Get its Share? (original) (raw)

Service Rating Prediction by Exploring Social Mobile User's Geographical Locations

Social media is for rating system presently a day's. Users update share or tag photos throughout their visits. The geographical data set by sensible phone bridges the gap between physical and digital worlds. Location data functions as a results of the affiliation between user's physical behaviors and virtual social web works structured by the sensible phone or net services user offers ratings to that place and this place becomes fashionable the help of rating prediction and user is used social media for rating. presently a day's social media becomes modern. We tend to tend to take a seat down with these social networks involving geographical knowledge as location-based social networks (LBSNs). Such knowledge brings opportunities and challenges for recommender systems to unravel the cold begin, meagerness drawback of datasets and rating prediction. Throughout this paper, we tend to tend to change use of the mobile users' location sensitive characteristics to carry out rating postulation. The affiliation between user's ratings and user-item geographical location distances, called user-item geographical affiliation, the affiliation between users' rating variations and user-user geographical location distances, called user-user geographical affiliation. Paper, we have a bent to vary use of the mobile users' location sensitive characteristics to carry out rating declaration.

Towards geosocial recommender systems

2012

Abstract The usage of social networks sites (SNSs), such as Facebook, and geosocial networks (GSNs), such as Foursquare, has increased tremendously over the past years. The willingness of users to share their current locations and experiences facilitate the creation of geographical recommender systems based on user generated content (UGC).

Reading the social preferences of tourist destinations through social media data

Back to the Sense of the City: International Monograph Book

The social preferences of individuals have been traditionally identified through traditional means using field techniques such as direct interviewing, observation and people-counting. The virtual layer of the social system currently allows new ways to identify the most preferred urban areas or venues. With that in mind, this paper aims to study how data from two Location-Based Social Networks: Foursquare and Twitter can shed light on empirical and theoretical observations about the spatial patterns characterizing where people tend to be and socialise in a tourist city. The methodology proposed consists of three stages. First, a self-developed desktop application retrieves geospatial data from the selected social networks. Then, the dataset obtained is organised and sorted. Finally, the georeferenced data is visualised and analysed and the trends are noted and discussed. To that end, the city of Benidorm was selected as a case study and the data was collected during the off-peak tour...

An analysis on the impact of geolocation in recommending venues in location-based social networks

2018

The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.

Geo-social recommendations

ACM Recommender Systems 2011 (RecSys) Workshop on Personalization in Mobile Applications, 2011

Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to “check-in” at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data ...