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This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.

The mobile device has been supported by the operating system that makes the phone has the potential to be a learning tool. The Android operating system is the mobile platform of high potential in education, plus it is open source, it can support independent learning. Availability of facilities and applications that can be installed offline, allowing an independent learning process starts from the students themselves. The purpose of this research is to find a practical model that can be applied in learning. Make mobile learning a practical model that can be used teachers, students and schools take advantage of the resources that already exist. The research method uses research and development, to mobile learning models that later developed into a model factual. Results The study concluded that: Android mobile application that is filled with interesting teaching materials can continue to invite progress within teacher as creator and students as users. Availability of Internet applications, making new teaching participants, can catch up with learning by opening apps without worry about the royalty claims. Android mobile application that supports online messaging greatly help communication between members. Results of the study are: a practical mobile learning model consists of four aspects, namely: Four Key Roles, Learning Tasks Accommodation, Interactive Content and the Modular Learning Outcomes.