Abstract:
Recommendation systems in the latest years have play an essential part and it is becoming an emerging trend in daily life and business. The Point-ofInterest (POI) - recommendation depends on the check-in histories of individual users at an explicit time. The goal is to develop a comprehensive recommendation system, which has the aptitude to learn the user long and short-term preference for the next POI recommendation and is a considerable attractive research interest. Since users check-in activities are independent and indefinable and can be viewed as long and short term sequence, which equally determine the user’s preference for the next destination. Moreover, the previous methods focuses on the geographical relationship of users among recently visited (POIs) and neglect the user's general interest. To addresses, the issue of (POI)-recommendation, the comprehensive model is given for learning user long and short term preferences. Which considers both recent successive information of users and their general taste simultaneously. The long term module utilizes fusion technique to capture the user's next (POI) preference, by computing the historical visit frequency of users to a particular venue representing their general interest. Whereas the short term preference module utilizes an attention mechanism, to learn the users’ recently visited locations at a specific time and location using spatiotemporal based attention model. The given model in this thesis has shown prominent results with an accuracy of 57.7% at N=30 for cold-start-users and 60.7% for Neural network and Random forest respectively. However these results is improved using on non-cold-start users to 79.1% and 77.9% at N=30. Besides the existing methods which neglect the user’s long-term behavior. This work targets to achieve improvement by using fusion model on real-word check-in information and access the model performance in terms of non-cold start and cold-start users which will overcome data sparseness problem in user check-ins, to achieve effective results, and will help in different applications of recommender system.