Computing and Information Systems - Theses

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End date: 2019 × Subject: Wikipedia × Subject: group recommendation ×

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    Personalized tour recommendation using location-based social media
    Lim, Kwan Hui ( 2017)
    Tourism is a popular leisure activity and an important industry, where the main task involves visiting unfamiliar Places-of-Interest (POI) in foreign cities. Recommending POIs and tour planning are challenging and time-consuming tasks for tourists due to: (i) the need to identify and recommend captivating POIs in an unfamiliar city; (ii) having to schedule POI visits as a connected itinerary that satisfies trip constraints such as starting/ending near a specific location (e.g., the tourist's hotel) and completing the itinerary within a limited touring duration; and (iii) having to satisfy the diverse interest preferences of each unique tourist. While tourism-related information can be obtained from the Internet, travel guides and tour agencies, many of these resources simply recommend individual POIs or popular itineraries, but otherwise do not appeal to the interest preferences of users or adhere to their trip constraints. In contrast to existing works on next-POI prediction and top-k POI recommendation that recommend a single POI or a ranked list of POIs, the task of tour recommendation involves the need to identify a set of interesting POIs and schedule them as an itinerary with various time and space constraints. While there are works on path planning that recommend an itinerary, this itinerary is typically optimized based on a global utility such as POI popularity, and thus offer no personalization for a tourist based on his/her interest preferences. This thesis addresses the challenges associated with the automation and personalization of tour recommendation using data mining techniques to model user interest and POI-related information, and using optimization problems and techniques to formulate and solve more realistic tour recommendation problems. Our main contributions include: (1) Proposing and implementing a framework that utilizes Flickr geo-tagged photos and Wikipedia to automatically determine user trajectories, interest preferences and POI-related information such as POI popularity and visiting times; (2) Proposing the PersTour algorithm for recommending personalized tour itineraries based on POI popularity, users' interest preferences and trip constraints, where POI visit durations are customized based on user interests; (3) Formulating the QueueTourRec problem for recommending queue-aware and personalized itineraries that schedule visits to popular and interesting POIs at times with minimal queuing times, and proposing a novel implementation of Monte Carlo Tree Search to solve this problem; (4) Developing the TourRecInt algorithm for tour recommendation based on a variant of the Orienteering problem with a mandatory POI category, which is defined as the POI category that a tourist has most frequently visited; (5) Formulating and solving the novel GroupTourRec problem, which involves recommending tour itineraries to groups of tourists with diverse interests and assigning tour guides with the right expertise to lead each tour group; (6) Illustrating the application of our proposed approach in practice, by presenting a web-based system implementation of our PersTour algorithm, with the front-end component developed using HTML, PHP, jQuery and the Google Maps API, and the back-end based on Python, Java and PHP.