TY - GEN
T1 - Non-linear book manifolds
T2 - 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2013
AU - Nock, Richard
AU - Nielsen, Frank
AU - Briys, Eric
PY - 2013
Y1 - 2013
N2 - Mainstream approaches in the design of virtual libraries basically exploit the same ambient space as their physical twins. Our paper is an attempt to rather capture automatically the actual space on which the books live, and learn the virtual library as a non-linear book manifold. This tackles tantalizing questions, chief among which whether modeling should be static and book focused (e.g. using bag of words encoding) or dynamic and user focused (e.g. relying on what we define as a bag of readers encoding). Experiments on a real-world digital library display that the latter encoding is a serious challenger to the former. Our results also show that the geometric layers of the manifold learned bring sizeable advantages for retrieval and visualization purposes. For example, the topological layer of the manifold allows to craft Manifold association rules; experiments display that they bring dramatic improvements over conventional association rules built from the discrete topology of book sets. Improvements embrace each of the following major standpoints on association rule mining: computational, support, confidence, lift, and leverage standpoint.
AB - Mainstream approaches in the design of virtual libraries basically exploit the same ambient space as their physical twins. Our paper is an attempt to rather capture automatically the actual space on which the books live, and learn the virtual library as a non-linear book manifold. This tackles tantalizing questions, chief among which whether modeling should be static and book focused (e.g. using bag of words encoding) or dynamic and user focused (e.g. relying on what we define as a bag of readers encoding). Experiments on a real-world digital library display that the latter encoding is a serious challenger to the former. Our results also show that the geometric layers of the manifold learned bring sizeable advantages for retrieval and visualization purposes. For example, the topological layer of the manifold allows to craft Manifold association rules; experiments display that they bring dramatic improvements over conventional association rules built from the discrete topology of book sets. Improvements embrace each of the following major standpoints on association rule mining: computational, support, confidence, lift, and leverage standpoint.
KW - Non-linear manifold learning
KW - Pattern mining
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=84882263164&partnerID=8YFLogxK
U2 - 10.1145/2467696.2467697
DO - 10.1145/2467696.2467697
M3 - Conference contribution
SN - 9781450320764
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 313
EP - 322
BT - JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries
Y2 - 22 July 2013 through 26 July 2013
ER -