Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Declarative Programming in Escher
Declarative Programming in Escher
IEEE Transactions on Knowledge and Data Engineering
Distances and (Indefinite) Kernels for Sets of Objects
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Hi-index | 0.00 |
In this paper, we present a novel and elegant hybrid recommender system called HOLCF, which use higher-order logic as data representation and integrate content and demographic information into a collaborative filtering framework by using higher-order logic distance computation approaches without the effort of feature construction. Our experiments suggest that the effective combination of various kinds of information based on higher-order logic distance computation approaches provides improved accurate recommendations than other approaches.