Introduction to algorithms
The Wasabi Personal Shopper: a case-based recommender system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Defining and Combining Symmetric and Asymmetric Similarity Measures
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
An Efficient Aproach to Similarity-Based Retrieval on Top of Relational Databases
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Partial Orders and Indifference Relations: Being Purposefully Vague in Case-Based Retrieval
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Case Retrieval Nets: Basic Ideas and Extensions
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Retrieving cases from relational data-bases: another stride towards corporate-wide case-base systems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
| Hi-index | 0.01 |
Case retrieval for e-commerce product recommendation is an application of CBR that demands particular attention to efficient implementation. Users expect quick response times from on-line catalogs, regardless of the underlying technology. In FindMe systems research, the cost of metric application has been a primary impediment to efficient retrieval. This paper describes several types of general and special-purpose ranking algorithms for case retrieval and evaluates their impact on retrieval efficiency with the Entree restaurant recommender.