All ETDs from UAB

Advisory Committee Chair

Ragib Hasan

Advisory Committee Members

Chengcui Zhang

Da Yan

Document Type


Date of Award


Degree Name by School

Master of Science (MS) College of Arts and Sciences


Recommendation Systems are becoming more popular with the development of web and mobile technologies. The Next Point-of-Interest (POI) Recommendation problem under the Recommendation Systems field is also one of the Recommendation Systems research areas. The main purpose of the Next POI Recommendation Systems is to analyze the past data of the users and give a new POI Recommendation to them. User data is derived from Location-based Social Networks (LBSNs) applications such as Foursquare, Gowalla, Yelp, etc. Next POI Recommendation Systems have many challenging factors because there are many parameters that affect the users' decision to next visit. Proposed Point-of-Interest Recommendation models in the literature have been reviewed in detail separately under traditional machine learning and deep learning subtitles. Using traditional machine learning algorithms for a Point-of-Interest Recommendation model from end to end cannot give users a new POI Recommendation with high accuracy compared to current results. However, traditional machine learning approaches are often used in deep learning to solve some part of the problem. Therefore, knowing about traditional machine learning approaches is the key to developing a state-of-the-art approach. Models developed using deep learning techniques outperform in terms of accuracy. One of the biggest challenges in Next POI Recommendation Systems is that there are many contexts that influence users' decisions such as static contexts, transition contexts, and dynamic contexts. We have developed a new Next POI Recommendation model using transition (spatio-temporal intervals, geographical probability), and dynamic contexts (time, weather, friendship relationship). The name of our model is the Attention-Based Deep Learning Model with Transition and Dynamic Contexts for Next POI Recommendation (ATDL-POI). Rich context input allows us to make customized recommendations for each user. We used deep learning techniques to benefit from the rich context and to learn the behavior of the users. Long short-term memory (LSTM) which is a deep learning model was utilized to understand the rich context and the complex behaviors relationships of users. LSTM has been customized by using power-law based geographical coefficient. Therefore, ATDL-POI has geographical distance probability awareness. When analyzing the past check-in data of the users, it was noticed that some check-ins had less effect on the user's future preferences. The short-term and long-term preferences of the users may be different. The attention mechanism was employed in ATDL-POI to solve this problem. ATDL-POI outperformed traditional machine learning algorithms, base LSTM, GRU, and many studies in the literature. This thesis study has been completed with both the detailed survey study and the proposed new model. Other context parameters or other deep learning models can be added/used to the proposed ATDL-POI model, which is a flexible approach open to improvements.



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