Knowledge-based recommender systems

By Robin D. Burke. 2000

1,004 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dc133144d431fc3b75c8de27f6bb21da6eb5bc1b

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Citations in the corpus

Hybrid Systems for Personalized Recommendations (2003, background): “Entree [4, 5 ] is a knowledge-based restaurant recommendation system that uses casebased reasoning [18] techniques to select and rank restaurants.”
Constraint-based recommender systems: technologies and research issues (2008, background): “, [6] [8] [38]), in which the user responds to”
Challenges and Solutions of Ubiquitous User Modeling (2012, background): “The demographic approach relies on a marketing approach which postulates that users with similar demographic backgrounds may have similar preferences.”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, unknown): “understand the difference between various cuisines [1, 28, 70].”
Hybrid Web Recommender Systems (2007, methodology): “This algorithm is described more fully in [9].”
Collaborative filtering recommender systems (2013, background): “Therefore KB approach can be a complement to other recommender approaches (Burke, 2000) Community-based: This kind of system works on the preferences of the users friends to recommend items.”
Adaptive interfaces and agents (2002, background): “A third approach is exemplified by the FINDME family of recommenders (Burke, Hammond, & Young, 1997; Burke, 2001).”
Ontological user profiling in recommender systems (2004, methodology): “The Entr´ ee [Burke 2000] restaurant recommender system, which uses a knowledge-based approach to recommendation, reports a recommendation accuracy of 38%, for 15 item pro.les, to 70%, for 5 item pro.les, based on analysis of historically logged user activity of a web site.”
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, background): “Several recommendation algorithms, such as content-based filtering [81], collaborative filtering [49], knowledge-based filtering [17] and their hybridizations [18], are widely discussed in the literature and in several surveys of the state-of-the-art [2].”
Recommender Systems (2016, unknown): “Case-based recommender systems are reviewed in [102, 116, 377, 558].”
Hybrid Recommender Systems: Survey and Experiments (2002, methodology): “Knowledgebased recommendation is at the heart of a research program known as “Find-Me Systems” (Burke et al. 1997; Burke 1999a; Burke, 2000).”
Critiquing-based recommenders: survey and emerging trends (2012, background): “These systems have also been termed “conversational recommender systems”, “conversational case-based reasoning systems”, and “knowledge-based recommender systems” (Smyth and McGinty 2003; Shimazu 2001; Burke et al. 1997; Burke 2000).”
Recommender system application developments: A survey (2015, unknown): “2 content-based (CB) [6] and knowledge-based (KB) [7] techniques.”
Hybrid recommender systems: A systematic literature review (2017, background): “items meet the users’ requirements, and generate recommendations accordingly [4].”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, background): “Hybrid recommendation systems can also be augmented by knowledge-based techniques [17], such as case-based reasoning, in order to improve recommendation accuracy and to address some of the limitations (e.g., new user, new item problems) of traditional recommender systems.”