Knowledge-based recommender systems (KBRs) are decision support systems that help users identify relevant items from a large set of alternatives. Unlike traditional methods like collaborative filtering and content-based filtering, KBRs use semantic user preference knowledge, item knowledge, and recommendation knowledge to identify user-relevant items, especially in complex and high-involvement scenarios. These systems are used when users specify and revise their preferences, and recommendations are based on constraints or attribute-level similarity metrics. This article provides an overview of the state-of-the-art in KBRs, explains different recommendation techniques using a working example from survey software services, and outlines future research directions. The article discusses three main recommendation approaches: collaborative filtering (CF), content-based filtering (CBF), and knowledge-based recommendation (KBR). CF uses user preferences of similar users to recommend items, while CBF recommends items similar to those the user has consumed. KBRs, however, use constraints or similarity metrics to recommend items that align with user preferences. KBRs are particularly useful in complex domains where user preferences change over time, such as financial services, software services, and apartment purchasing. The article also explores various further recommendation approaches, including hybrid systems that combine different methods, group recommenders that support recommendations for groups, and knowledge-based group recommenders. It discusses the importance of constraint-based recommendation, which uses constraints to determine recommendations, and case-based recommendation, which uses past examples to guide recommendations. The article highlights the challenges and opportunities in KBRs, including the need for user preference elicitation, the use of constraint satisfaction problems, and the integration of machine learning techniques. It also discusses the importance of handling inconsistent requirements and providing explanations for recommendations. The article concludes with a discussion of future research directions in KBRs, emphasizing the need for further exploration of hybrid recommendation, search optimization, explanations, and conversational recommendation.Knowledge-based recommender systems (KBRs) are decision support systems that help users identify relevant items from a large set of alternatives. Unlike traditional methods like collaborative filtering and content-based filtering, KBRs use semantic user preference knowledge, item knowledge, and recommendation knowledge to identify user-relevant items, especially in complex and high-involvement scenarios. These systems are used when users specify and revise their preferences, and recommendations are based on constraints or attribute-level similarity metrics. This article provides an overview of the state-of-the-art in KBRs, explains different recommendation techniques using a working example from survey software services, and outlines future research directions. The article discusses three main recommendation approaches: collaborative filtering (CF), content-based filtering (CBF), and knowledge-based recommendation (KBR). CF uses user preferences of similar users to recommend items, while CBF recommends items similar to those the user has consumed. KBRs, however, use constraints or similarity metrics to recommend items that align with user preferences. KBRs are particularly useful in complex domains where user preferences change over time, such as financial services, software services, and apartment purchasing. The article also explores various further recommendation approaches, including hybrid systems that combine different methods, group recommenders that support recommendations for groups, and knowledge-based group recommenders. It discusses the importance of constraint-based recommendation, which uses constraints to determine recommendations, and case-based recommendation, which uses past examples to guide recommendations. The article highlights the challenges and opportunities in KBRs, including the need for user preference elicitation, the use of constraint satisfaction problems, and the integration of machine learning techniques. It also discusses the importance of handling inconsistent requirements and providing explanations for recommendations. The article concludes with a discussion of future research directions in KBRs, emphasizing the need for further exploration of hybrid recommendation, search optimization, explanations, and conversational recommendation.