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Resto Xplorer: Food Searching Web Portal to Assist Tourists in Nepal 

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Resource details

Resource ID

202

Access

Open

Contributed by

Rajan Karmacharya

Author/Contributor

Raktim Shrestha, Rajan Karmacharya (Supervisor)

Publisher/Credit

St. Xavier's College

Date

28 April 16

Document type

Thesis or project

Department

Computer science

Level

Bachelor

Keywords

Restaurant, Recommendation System, Location Based System, Mobile
Phone Internet.

Abstract

Eating has always been a sociable event, from primitive campfire cooking to the modern cuisines at the hotels and restaurants these days. People tend to eat at their workplaces, colleges and surrounding’s. Time is a quintessential factor even while dining. People love to eat at the places where they find good services and high quality foods at cheap prices. Context information, such as user location, time, and user profile, has been popularly applied to analyze user behavior for many societies.
Products and services in the field of tourism (like hotel rooms, packages, etc.) are mainly not physical and typically exists mostly as information. For this reason, they are predestinate for electronic sale. The Information Communications Technologies (ICT) plays a major role in tourism, travel and hospitality industry. The Integration of ICT in the tourism industry is an essential for success of tourism enterprise. ICT facilitates an individual to access the tourism products information from anywhere any time. ICT allows easily to present tourism offerings with richer descriptions to enable travelers to make more informed choices. Tourism enterprises can also reach the targeted customers across the globe in a single click on the keypad after emergence of mobile computers, web technologies etc.
The project explores tools and techniques to develop a restaurant recommendation system based on mobile context-aware services to provide customized information for users. We analyze the service satisfaction ratings of the users to recommend favored restaurants for them. It ubiquitously studies the user's behavioral pattern of visiting restaurant using a user history. With mobile context awareness, the proposed framework can substantially enhance the capacity to satisfy the user demands for restaurant recommendations.

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