Finding suitable places for live campaigns using location-based services

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

“…The study showed an average root mean square error (RMSE) reduction of 87.50%. Another prediction model to place locations for new businesses were developed in [13,14,26]. The next restaurant branch was predicted using three types of features: review-based market attractiveness, review-based market competitiveness, and geographic proprieties of a candidate location restaurant [13].…”

“…The study revealed that GBRT yielded the best results. The optimal location to place a live campaign by predicting the potential number of spectators was studied in [14] based on a dataset collected for New York city from Foursquare; the features used in the study included area density, area popularity, neighbor entropy, the degree of change in area popularity, the degree of uniqueness in the total number of check-ins, the amount of available open area for the live campaign, and the most time slot the people visit this area, and the SVM had an accuracy of 72.6% [14].…”

“…The list in Table III shows that the categories covered by exiting literature were either single or multiple categories. [10] Starbucks, Dunkin' Donuts, McDonald [12] Venues with Similar category [25] Multiple categories [13] Restaurants [14] Live Campaigns [11] Starbucks [26] Food business [27] Restaurants, Nightlife, Active Life, Shopping, Hotels, Beauty and Spas, Food, Arts and Entertainment, Religious Organizations and Mass Media [28] Event [29] Billboards [30] Hotels, Shops, Services, Gym and Restaurants. [32,35] Coffee shops, Express inn [33] Retail Shop [34] Hotels…”

“…Prediction of the potential number of visitors to a venue in a city is considered a city economy application that can be built based on PSS human mobility data. Many researchers model the venue-popularity prediction problem as a typical featuretarget machine-learning problem and do not consider the performance of these models on the dataset of venues with different distributed categories [10,13,14]. This emphasizes the need for investigating different modeling for the venuepopularity prediction problem with venues with different distributed categories.…”

Random Forest with Harmony Search Optimization for Location Based Advertising

“…The expected audiences were predicted at a location on the basis of those features. The individual features are achieved based accuracy as 50.46% and 72.6% accuracy in the regression model of Support Vector Machine (SVM) [24].…”