In many domains, cases are associated with each other though this is not easily explained by the set of features they share. It is hard, for example to explicitly enumerate features that make a movie romantic. We present an extension to the Case Retrieval Network architecture, a spreading activation model initially proposed by Burkhard and Lenz, by allowing cases to influence each other independently of the features. We show that the architecture holds promise in improving effectiveness of retrieval in two distinct experimental domains.
CITATION STYLE
Shekhar, S., Chakraborti, S., & Khemani, D. (2014). Linking cases up: An extension to the case retrieval network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8765, 450–464. https://doi.org/10.1007/978-3-319-11209-1_32
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