INTELLIGENT AGENT FOR TOURISM RECOMMENDATION SYSTEM

Jay Borade, Shraddha Makwana, Puja Gupta, Sayalee Lanjewar

Abstract: The paper proposes recommendation system for places in tourism domain like food, hotels and travel places and also consists of methods and algorithms for generating such recommender system. Tourism is one of the most important and dynamic industry which needs to accommodate the daily changing requirements of the user. It is one of the most interesting research areas and hence requires a strong recommendation system. In order to find places based on user’s criteria in traditional manner would be much ambiguous as it would cost user to find places in separate systems or sometime in order to find such places user has to walk and find information related to that place, but when we use machine learning algorithms and data mining technique to integrate food, hotel and travel based recommendation at one single place it will improve performance in timely manner as well as will make it convenient for the user. Proposed System currently works on data sets of around 7000 records and suggests user from that data. Further system can be expanded to even large data sets. The system shows how qualitative approach can be used with minimal user interface. It gives around more than 80% appropriate results when tested at various locations and it can be further improved with more real time data sets. The paper provides the architecture of our system with implementation details and results. It also explains the flow of the proposed system which covers complete process to be followed, from user login and filling the food survey form to getting different categories of recommendations.

Keywords: Recommendation, algorithms, restaurants, tourism, django

DOI: https://doi.org/10.15623/ijret.2018.0704007