Computing and Information Systems - Theses
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ItemPersonalized tour recommendation using location-based social media( 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.
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ItemComputing relationships and relatedness between contextually diverse entities( 2011)When presented with a pair of entities such as a ball and a bat, a person may make the connection that both of these entities are involved in sport (e.g., the sports baseball or cricket, based on the individual's background), that the composition of the two entities is similar (e.g., a wooden ball and a wooden stick), or if the person is especially creative, a fancy dress ball where someone has come dressed as a bat. All of these connections are equally valid, but depending on the context the person is familiar with (e.g., sport, wooden objects, fancy dress), a particular connection may be more apparent to that person. From a computational perspective, identifying these relationships and calculating the level of relatedness of entity pairs requires consideration of all ways in which the entities are able to interact with one another. Existing approaches to identifying the relatedness of entities and the semantic relationships that exist between them fail to take into account the multiple diverse ways in which these entities may interact, and hence do not explore all potential ways in which entities may be related. In this thesis, I use the collaborative encyclopedia Wikipedia as the basis for the formulation of a measure of semantic relatedness that takes into account the contextual diversity of entities (called the Related Article Conceptual Overlap, or RACO, method), and describe several methods of relationship extraction that utilise the taxonomic structure of Wikipedia to identify pieces of text that describe relations between contextually diverse entities. I also describe the construction of a dataset of museum exhibit relatedness judgements used to evaluate the performance of RACO. I demonstrate that RACO outperforms state-of-the-art measures of semantic relatedness over a collection of contextually diverse entities (museum exhibits), and that the taxonomic structure of Wikipedia provides a basis for identifying valid relationships between contextually diverse entities. As this work is presented in regard to the domain of Cultural Heritage and using Wikipedia as a basis for representation, I additionally describe the process for adapting the principle of conceptual overlap for calculating semantic relatedness and the relationship extraction methods based on taxonomic links to alternate contextually diverse domains, and for use with other representational resources.