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Restaurant Reviews According to Geographical Location

1. Abstract

Most of us have thought about where to go while eating, but we may not be able to decide exactly what we want to choose. Especially if we are going to eat from a place we have not been to before and we have no idea whether this place is good or bad, it can become an inextricable task. We decided to make an application in order to prevent this situation. Today, people spend an average of 248 minutes a day looking at their mobile devices and they aim to reach the information they want to learn quickly and easily. After knowing this information we decided to make a mobile application. This project sorts the restaurants around the users according to their scores and distance from the user, and shows other comments made to the chosen restaurant by the other users. At the same time, it uses a captcha-style for Sign in to prevent bots from commenting.

2. Introduction

Thanks to this project, users will be able to learn how the restaurant food is, whether they comply with the hygiene rules, without going to certain restaurants, they will be able to post pictures to support what they say, and they will be able to decide whether the comments made by others are useful or useless. The number of active internet users has increased by 1036% in the last 20 years (413 million to 4.66 billion)[6,7] and according to the documents published by Science, false stories and documents are shared faster than the truth [8]. Since we estimate how much a user who uses a commenting site or application can trust this information in an environment where misinformation spreads so quickly and comfortably, we will first ask a commenter to share his/her comment with a picture that supports his/her comment. Since we think that it will not be completely reliable, we will authorize users to evaluate other users' comments (as useful and useless). Comments with a certain amount of useless evaluations higher than useful will be automatically deleted by the system. Likewise, there may be business owners who want to increase their restaurant's score, so they can log in to comment. Required and to login a small captcha approval is required. While thinking about this project, we will add an option to show restaurants on the map, considering that there may be people who have just come to the city (There will be a certain score with the distance to the user and the points given by other users. Restaurants will appear on the map in different colors according to this score (from green to red).) so that the user will be able to read the reviews of the restaurant that is more attractive to him and choose whether he wants to go there.) Likewise, if the user knows what he wants to eat but does not know which restaurant he wants to go to, we can filter certain types of restaurants by searching with certain keywords. We will chart a path.

3. Related Works

Mobile apps are the easiest way to order food. It has been observed that there is an exponential growth in online food ordering due to the rapid increase in mobile users. Consumers find it very easy to search websites or apps to select dishes from restaurants they like. Reviews and ratings given by consumers on websites are helpful in making purchasing decisions for new customers. Filters are also added to websites and mobile apps to categorize and customize the order according to the consumer's needs. Everyone is a stakeholder in business. However, the most important stakeholder is the customer. Delivery time is the most important and decisive factor in customer retention. Company employees also know that delaying an order means consumers are more likely to switch to other food ordering and delivery services. Food delivery companies understand the importance of delivery time very well, so they provide live tracking order facility to know the arrival time of their orders. Order tracking is completely dependent on GPS (global positioning system), meaning the delivery agent needs to enable GPS service on mobile or vehicle so that customer and consumer can track the cargo on mobile phone. Relationships are the key to success in the service industry, so companies focus more on building strong relationships with customers. In food delivery services, the company does not take ownership of the food flavor. This responsibility is taken by the restaurant owners. The main concern of the meal delivery service is to provide the food to the door of the consumer and this within the promised time frame (William R. King, Jun He, 2006) [4].

The development of technology in the field of information and communication, especially the Internet, facilitates consumers' access to kitchen information, and plays an important role in enabling kitchen entrepreneurs to market their products and develop a wider market. The development of technology brings along the need for mobility, especially the high use of smartphones. The rapid increase in smartphone usage has led to two billion active smartphone users worldwide in 2016, with Indonesia one of the countries with the third largest growth in smartphones, according to the Emarketer report. Indonesia is also expected to be the fourth largest smartphone user, with nearly 100 million active smartphone users in 2018. Culinary developments in Indonesia are accompanied by advances in mobile device technology and increased the mobility of consumer activities, thus eliminating the need for locations and restaurants. information emerges quickly and easily. This study aims to develop applications that can help consumers find information on mobile devices using Location Based Service (LBS) technology. Current technology also makes it easy for programmers to make this application, one of which is Location Based Service (LBS). Location-Based Service (LBS), is a service accessible via mobile devices that displays information connected to a network and can indicate the geographic location of the device or the location of a place like to find the nearest restaurant. Location Based Service (LBS) will facilitate users to search for remote areas and also what information is available at user-acquired location (Layona & Yulianto, 2016) [1].

Online food ordering has been adopted by most of the restaurants that offer food delivery. Customers using online food ordering showed gratitude to the technology and stated that online ordering met expectations. The advantages of ordering online are improved order certainty, high throughput, and improved customer relationship management. These will likely eliminate costs and operational threats for most restaurants. The survey found that when a consumer decides to purchase food online, it is influenced by multiple factors. The main key factors identified are time savings and convenience. People compare prices on the online meal delivery website and apps, and then review all product feedback and ratings before making their final choice of dish. Therefore, restaurants should make appropriate strategies to increase consumer confidence by receiving feedback, to encourage customers to share reviews about their food, and also to raise awareness of their online food market presence by displaying food products online (Girish Deore1 & Pranav Shete, 2016) [2].

Online food delivery services in urban areas rely heavily on urban transport due to the rather heavy traffic in cities. These services use user-generated content to encourage collaborative consumption among their members. The researcher evaluated the impact of traffic conditions (using the Google Maps API), key performance indicators of online food ordering and delivery services. From the overall research, it was found that although early deliveries showed a quality issue association with the number of comments made by customers after receiving orders at the door, traffic conditions had no practical effect on throughput and delivery time fulfillment (Juan C. Correa, 2017) [3].

If online food ordering services offer a delivery option, the customer will be more satisfied and a confirmation email will be sent to the customer regarding the order status. In the current scenario, every online food ordering and delivery service company has a mobile app in various app stores, and almost many people carry smartphones in urban areas. When an order is placed through the mobile application, the customer can track their orders via the mobile application, thanks to the GPS installed on each smartphone. The app also shows the estimated time of delivery (Shantashree Das, Debomalya Ghose, 2019) [5].

In the article named A Mobile Location-based Information Recommendation System Based on GPS and WEB2.0 Services they used gps systems to draw a map around user and show restaurants around them in map we will also use this but we will make restaurants clickable that would show user reviews for that restaurant and show their points, also acording to article using Tagging(Tagging means binding an keyword that is not a part of coding with information within computer[11]) would help user to reach what is he looking for faster so after looking at this article we decided to add a little bit of tagging to our data.they also used coloring to change tag colors according to least used and most used tags we used this in our project’s map function which shows closer restaurants with good score in green and far or bad scored ones with red[10].

Yemeksepeti is actually a food ordering app but in Yemeksepeti you could also look at comments made for any restaurant, but in Yemeksepeti you can’t add images to food you ordered or you can't vote any comment done for any restaurant, unlike in Yemeksepeti in our project we could upvote, downvote other reviews add images of food and you could even have road map to the destination [9].

4. Background

4.1. Location-Based Services (LBS)

LBS is the term that describes the technology used to locate the device. LBS is basically finding users Location by taking signal from users telephone catching that signal from satellites and resending that location in mathematical location to user. After finding the user's location, use that location to give services. LBS are composed of two parts; first part is Location Manager second part is Location Providers [12,13] .

4.2. Location Manager (API Maps)

Location Manager is something like a middleman. It takes data from Location Providers and helps the user to process that data like getting mathematical values turning them into maps or changing maps or changing the view of the user like satellite views etc. This package is located at com.google.android.maps.

4.3. Location Providers (API Locations)

By detecting displacement, users can determine their location, track movement, and determine proximity to a particular location. Currently, it is built from new information and communication technologies (New Information and Communication Technologies / NICTS), mobile telecommunications systems and handheld devices, data from the Internet and Geographic Information Systems (GIS) using spatial databases. Location Based Services has five key components, including:

• Mobile Devices: A tool used by the user to request the required information. Information can be provided in the form of sound, images, and text.

• Communication Network: A communication network is needed to first encapsulate the users data and the information that is requested and send that to the service provider.Service providers would send the requested information to the user by user data. Communication Network could be one of the three; Cellular network(GSM, CDMA), Wireless Wide Area Network(WWAN), Wireless Local Area Network.

• Positioning Component: When a service is running it needs the user's current position.

• Service and Application Provider: Service and Application providers present their services to users and wait for user feedback to improve their works.

• Data and Content Provider: As Service providers are not responsible for saving all the required data. Data and content providers are needed to request data.

4.4. Captcha

Captcha is an test that stops automated sign in process.Captcha is a test that Gives an text in what is wanted is written and different photos and ask user to choose which photos are related to text.For average human it takes 10 sec[14] to solve a captcha as it is easy for a human brain to read a text and create a image that corresponds to that text but for a computer it firstly have to know what the text means than check for images that corresponds to that and find images closer to the image the computer finds so it is harder for computer than human.[15]

5. Conclusion

The results that can be obtained from the application of a geographic information system to use the location search of a restaurant in the province of Ankara with an Android-based local-based service method are:

• The closest restaurant using a smartphone as this media application.

• This system can make it easier for users to find the nearest restaurant, so it is easier for users to determine which restaurant to visit.

• The generated Google Maps road data is more complete to support a more diverse route selection.

There are a lot of Review systems and GPS systems but there are not many applications that combine those two systems to create a new one. In our system we hope to create an application that would help users to find new places to go when they are hungry, help them locate hidden nice restaurants in hard to find places. Users don’t even need to go to that location to know if that place is good or bad as other reviewers would score that place already. User’s could also get achievements for scoring a restaurant that doesn’t have any reviews or scores. In conclusion we hope to create a Restaurant reviewing application that would help users to find new locations to eat food and enjoy.

6. References

1. Layona Rita & Budi Yulianto, An Implementation of Location Based Service (LBS) for Community Tracking. Accessed on https://journal.binus.ac.id/index.php/comtech/article/view/3749/3129 .

2. Girish Deore1, Pranav Shete. To Study the Inclination of Consumers in Baner Area in Relation to the Online Food Ordering. Accessed on https://hmct.dypvp.edu.in/Documents/Girish-Paper.pdf .

3. Juan C. Correa, Phillip Broker, Gopal Sakarkar. Urban Mobility and Food Ordering Services: A Web Mining Perspective. Accessed on https://www.researchgate.net/publication/319493783_Urban_Mobility_and_Food_Ordering_Services_A_web_mining_perspective .

4. William R. King, Jun He. A meta-analysis of the technology acceptance model. Accessed on https://www.researchgate.net/publication/222297603_A_meta-analysis_of_the_Technology_Acceptance_Model#read .

5. Shantashree Das, Debomalya Ghose. Influence Of Online Food Delivery Apps On The Operations Of The Restaurant Business. Accessed on http://www.ijstr.org/final-print/dec2019/Influence-Of-Online-Food-Delivery-Apps-On-The-Operations-Of-The-Restaurant-Business-.pdf

6. https://ourworldindata.org/internet#:~:text=Globally%20the%20number%20of%20internet,online%20for%20the%20first%20time.

7. https://www.oberlo.com/blog/internet-statistics

8. https://www.science.org/doi/ 10.1126/science.aap9559

9. https://tr.wikipedia.org/wiki/Yemeksepeti

10. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.590.1029&rep=rep1&type=pdf

11. http://en.wikipedia.org/wiki/Tag_(metadata)

12. Koeppel, I. What are location services? From a GIS Perspective, ESRI white paper.2000. Accessed on: https://www.researchgate.net/publication/222679581_Location_Based_Services_and_GIS_in_Perspective

13. Shiode, N., Li, C., Batty, M., Longley, P., & Maguire, D. The impact and penetration of location-based services. In H. A. Karimi & A. Hammad (Eds.), Telegeoinformatics: location-based computing and services, 2004, pp. 349–366, CRC Press. Accessed on: https://www.researchgate.net/publication/32884883_The_impact_and_penetration_of_location-based_services

14. Bursztein, Elie; Bethard, Steven; Fabry, Celine; Mitchell, John C.; Jurafsky, Dan (2010). "How Good are Humans at Solving CAPTCHAs? A Large Scale Evaluation" (PDF). Proceedings of the 2010 IEEE Symposium on Security and Privacy: 399–413. CiteSeerX 10.1.1.164.7848. doi:10.1109/SP.2010.31. ISBN 978-1-4244-6894-2. S2CID 14204454. Retrieved March 30, 2018. Accessed on: https://web.stanford.edu/~jurafsky/burszstein_2010_captcha.pdf

15. https://en.wikipedia.org/wiki/CAPTCHA

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