TY  - CHAP
ID  - loza14recsys
T1  - A Hybrid Multi-Strategy Recommender System Using Linked Open Data
A1  - Ristoski, Petar
A1  - Loza Mencía, Eneldo
A1  - Paulheim, Heiko
TI  - Semantic Web Evaluation Challenge, Proceedings (ESWC 2014)
T3  - Communications in Computer and Information Science
Y1  - 2014
VL  - 475
SP  - 150
EP  - 156
PB  - Springer
SN  - 978-3-319-12023-2
UR  - http://2014.eswc-conferences.org/sites/default/files/eswc2014-challenges_rs_submission_12.pdf
M2  - doi: 10.1007/978-3-319-12024-9_19
N2  - In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.
ER  -