How can explanations be tailored to different types of users and contexts?

In this paper, the quality of decision making for hotel recommender systems based on sentiment analysis, deep learning, and data balancing techniques has been ... Dec 20, 2024 ... While machine learning helps to process recommendation lists, it ... Over time, reinforcement learning ensures the algorithm learns ... Jul 25, 2023 ... Common machine-learning models for recommendation algorithms include neural networks and k-means clustering (kNN). Keep in mind that real-time ... Dec 31, 2022 ... The main goal of this proposal is to develop a system for recommending skincare products based on the user's skin type and the makeup of the ... Oct 13, 2022 ... Chemical compositions registered in the inorganic crystal structure database (ICSD) were supplied to machine learning for binary classification. The proposed livestreaming e-commerce recommendation system (LERS) enhances the exploration of new items by incorporating uncertainty into neural networks. Mar 28, 2025 ... Recommendation systems use data-driven methodologies to provide users with tailored suggestions. These systems utilize user data and ... Jan 28, 2025 ... A recommendation engine is a system that analyzes user behavior, preferences, and historical interactions to suggest relevant content, products, ... Jun 19, 2024 ... Recommendation systems rely on big data analytics and machine learning (ML) algorithms to find patterns in user behavior data and recommend ... Content-based filtering recommends items based on the similarity in features between an item and a user preference.