Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations (Mar-2017)
The main aim of the project user geographical information located by smart phone bridges the gap between physical and digital worlds. Location data functions as the connection between user’s physical behaviors and virtual social networks structured by the smart phone.
It also allows users to share their experiences such as reviews ratings photos check-ins and moods in LBSNs with their friends. Such information brings opportunities and challenges for recommender systems. The geographical location information bridges the gap between the real world and online social network services. User search a restaurant considering convenience we will never choose a faraway one this can help us to constrain rating prediction users take a long distance travelling a far away new city as strangers.
A recommender systems consider geographical location factor the recommendations may be more humanized and thoughtful. The relevance between user’s ratings and user item geographical location distances called as user-item geographical connection and to mine the relevance between users rating differences and user-user geographical location distances connection to chart. To find the people whose interest is similar to users three factors are taken into consideration for rating prediction. These factors are fused into a location based rating prediction model and relevance between ratings and user item geographical location distances.
It is discovered that users usually give high scores to the items which are very far away from their activity centers. It can help us to understand users’ rating behaviors for recommendation. The relevance between users’s rating differences and geographical distances. It can help us to understand users’ rating behaviors for recommendation, for user-user geographical connection and interpersonal interest similarity into a Location Based Rating Prediction model.