Location-based recommendation is a recommender system that incorporates location information, such as that from a mobile device, into algorithms to attempt to provide more-relevant recommendations to users like Google Maps
The application can be used on social places, restaurants and other people interest near user location
Facebook and Twitter are using location information and tend to show the recommened events, local trends near to users with increasing popular usage of smart phones
The Recommentation systems mainly classified on two ways, collaborative or content-based, the location based will be mostly on collaborative side Collaborative - User’s past behavior as well as similar decisions made by other users Content based - Utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties
People using smart phones with social apps like facebook, twitter, instagram etc
Digital Marketting, crowd gathering, people interest, Advertisement
Improve the business of adversting companies and business model
Reverse Engineering
The providers of a recommendation engine may introduce some stochasticity into their recommendation process, making this even harder. To give the final point to convince you this is not a worthy endeavor, now most of the recommendation are more positive than negative, like below
The Reverse engineering should recommend as below “Hey, It is not recommended to go that place at dark evening, because user reported that there is no street lights and hard for vehicles to travel”
Suggestion
More roubust algorithms and hybrid models needs to built with help of AI and Machine learning in order improve the recommnder systems
References
https://www.researchgate.net/publication/312480566_A_comparative_study_of_location-based_recommendation_systems https://www.hindawi.com/journals/cin/2016/1291358/