Computing and Information Systems - Theses

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Subject: machine learning × Subject: data mining × Author: Xue, Yuan ×

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    Recommendation systems for travel destination and departure time
    Xue, Yuan ( 2015)
    People travel on a daily basis to various local destinations such as office, home, restaurant, appointment venue, and sightseeing spot. It is vital to most people that we have a positive experience and high efficiency of daily travel. With this observation, my research strives to provide daily-travel related recommendations by solving two optimisation problems, driving destination prediction and departure time recommendation for appointments. Our “SubSyn” destination prediction algorithm, by definition, predicts potential destinations at real-time for drivers on the road. Its applications include recommending sightseeing places, pushing targeted advertisement, and providing early warnings for road congestion. It employs the Bayesian inference framework and second-order Markov model to compute a list of high-probability destinations. The key contributions include real-time processing and the ability to predict destinations with very limited amount of training data. We also look into the problem of privacy protection against such prediction. The “iTIME” departure time recommendation system is a smart calendar that reminds users to depart in order to arrive at appointment venues on time. It also suggests the best transport mode based on users’ travel history and preferences. Currently, it is very inefficient for people to manually and repeatedly check the departure time and compare all transport modes using, for instance, Google Maps. The functionalities of iTIME were realised by machine learning algorithms that learn users’ habits, analyse the importance of appointments and optimal mode of transport, and estimate the start location and travel time. Our field study showed that we can save up to 40% of time by using iTIME. The system can also be extended easily to provide additional functionalities such as clashing appointments detection and appointment scheduling, both taking into account the predicted start location and travel time of future appointments. Both problems can be categorised as recommender systems (or recommendation systems) that provide insightful suggestions in order to improve daily-travel experiences and efficiency.