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Travel Itinerary Recommendation for Real World Point of Interests Using Iterated Local Search
Almira C.a, Maulidevi N.U.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Before going travel, many things must be prepared. One of the most important things is planning travel itinerary. When planning travel itinerary, there are some steps to do, such as searching the point of interests to visit, arrange the point of interests visiting order, select which point of interests that will be visit in the same day, etc. Planning by searching through the internet, magazine, pamphlet or other information sources can take some times because the number of information is almost unlimited nowadays. Recommender system is one of the option to overcome that problem. This paper will describe about building travel itinerary recommender system. Travel itinerary recommender system can be modeled as a problem of creating route with time constraint for traveling, which is called Tourist Trip Design Problem (TTDP) in scientific literature. The fastest and most efficient algorithm found so far for solving the problem is Iterated Local Search (ILS). Therefore, this paper uses ILS algorithm for solving TTDP for real world point of interests. The difference with other literature is that in this paper, there are several additional constraints that make the recommendation applicable for real-world point of interests. The recommender system was built as a web prototype. The prototype was tested by 30 respondents for measuring the recommendation quality and it got a satisfactory result.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Algorithm for solving,Design problems,Information sources,Iterated local search,Point of interest,Scientific literature,Time constraints,Travel itinerary[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Iterated Local Search,recommendation system,Tourist Trip Design Problem,travel itinerary[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICAICTA.2019.8904339[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]