The paper introduces CP-nets, a qualitative graphical representation for expressing and reasoning about conditional ceteris paribus (all else being equal) preference statements. CP-nets are designed to capture the intuitive nature of such preferences, which are often used in various decision-making contexts, including collaborative filtering, recommender systems, product configuration, and medical decision making. The authors provide a formal semantics for CP-nets, defining the structure and properties of these networks, and discuss their expressive power and computational aspects. Key features of CP-nets include: 1. **Qualitative Representation**: CP-nets use conditional preference tables (CPTs) to specify preferences over variables, allowing for a compact and intuitive representation. 2. **Ceteris Paribus Semantics**: Preferences are expressed as "all else being equal," making them natural and intuitive for users. 3. **Inference Techniques**: The paper describes algorithms for performing inference tasks such as outcome optimization, dominance queries, and ordering queries. 4. **Acyclic Networks**: Acyclic CP-nets are shown to be satisfiable, ensuring consistency in preference rankings. 5. **Application Examples**: The authors provide examples of CP-nets in action, including a multimedia document presentation system where preferences are used to dynamically adjust the display based on user choices. The paper also discusses the challenges and limitations of cyclic CP-nets, emphasizing the importance of acyclic networks for practical applications. Overall, CP-nets offer a flexible and structured approach to representing and reasoning about conditional preferences, making them a valuable tool in automated decision-making systems.The paper introduces CP-nets, a qualitative graphical representation for expressing and reasoning about conditional ceteris paribus (all else being equal) preference statements. CP-nets are designed to capture the intuitive nature of such preferences, which are often used in various decision-making contexts, including collaborative filtering, recommender systems, product configuration, and medical decision making. The authors provide a formal semantics for CP-nets, defining the structure and properties of these networks, and discuss their expressive power and computational aspects. Key features of CP-nets include: 1. **Qualitative Representation**: CP-nets use conditional preference tables (CPTs) to specify preferences over variables, allowing for a compact and intuitive representation. 2. **Ceteris Paribus Semantics**: Preferences are expressed as "all else being equal," making them natural and intuitive for users. 3. **Inference Techniques**: The paper describes algorithms for performing inference tasks such as outcome optimization, dominance queries, and ordering queries. 4. **Acyclic Networks**: Acyclic CP-nets are shown to be satisfiable, ensuring consistency in preference rankings. 5. **Application Examples**: The authors provide examples of CP-nets in action, including a multimedia document presentation system where preferences are used to dynamically adjust the display based on user choices. The paper also discusses the challenges and limitations of cyclic CP-nets, emphasizing the importance of acyclic networks for practical applications. Overall, CP-nets offer a flexible and structured approach to representing and reasoning about conditional preferences, making them a valuable tool in automated decision-making systems.