Tuesday, May 24, 2022 - 08:30 to 10:00
SCHOOL OF COMPUTER SCIENCE
The School of Computer Science is pleased to present…
MSc Thesis Defense by: Farzaneh Jouyandeh
Date: Tuesday May 24, 2022
Time: 8:30 am – 10:00 am
Meeting URL: https://us06web.zoom.us/j/87577929482?from=addon
Passcode: If interested in attending this event, contact the Graduate Secretary at email@example.com with sufficient notice before the event to obtain the passcode.
The tourism industry plays a vital role in today's world. Many people travel around the world to visit and explore other places. However, planning an itinerary is one of the most challenging and time-consuming tasks for many travelers. It could be even more complicated when they travel as a group with different constraints and various choices of points of interest (POIs).
The problem of group itinerary recommendation is an extension of the orienteering problem and is NP-hard, which can be defined as an optimization problem. This research addresses the problem by proposing a personalized group itinerary recommendation algorithm using cultural algorithms. Cultural algorithms are evolutionary algorithms that use knowledge to guide the search direction during the evolution process. The main objective of our proposed model is to maximize the group's satisfaction by optimizing the number of visiting POIs, while considering the interests of all users, travel time, visit duration, and budget. The performance of our proposed model has been evaluated on real-world datasets and compared with the existing methods. The results depicted that our proposed algorithm outperforms other state-of-the-art algorithms on both datasets in most of the experiments in terms of the quality of the final solution. Furthermore, our evaluation demonstrated that the proposed approach generates consistent results in various situations and is notably different from existing algorithms.
Keywords: Itinerary recommendation, Cultural algorithm, Optimization
MSc Thesis Committee:
Internal Reader: Dr. Ziad Kobti
External Reader: Dr. Rashid Rashidzadeh
Advisor: Dr. Pooya Moradian Zadeh
Chair: Dr. Kalyani Selvarajah
MSc Thesis Defense Announcement
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