TY - GEN
T1 - Towards Similarity-Aware Constraint-Based Recommendation
AU - Atas, Müslüm
AU - Tran, Thi Ngoc Trang
AU - Felfernig, Alexander
AU - Polat Erdeniz, Seda
AU - Samer, Ralph
AU - Stettinger, Martin
PY - 2019
Y1 - 2019
N2 - Constraint-based recommender systems help users to identify useful objects and services based on a given set of constraints. These decision support systems are often applied in complex domains where millions of possible recommendations exist. One major challenge of constraint-based recommenders is the identification of recommendations which are similar to the user’s requirements. Especially, in cases where the user requirements are inconsistent with the underlying constraint set, constraint-based recommender systems have to identify and apply the most suitable diagnosis in order to identify a recommendation and to increase the user’s satisfaction with the recommendation. Given this motivation, we developed two different approaches which provide similar recommendations to users based on their requirements even when the user’s preferences are inconsistent with the underlying constraint set. We tested our approaches with two real-world datasets and evaluated them with respect to the runtime performance and the degree of similarity between the original requirements and the identified recommendation. The results of our evaluation show that both approaches are able to identify recommendations of similar solutions in a highly efficient manner.
AB - Constraint-based recommender systems help users to identify useful objects and services based on a given set of constraints. These decision support systems are often applied in complex domains where millions of possible recommendations exist. One major challenge of constraint-based recommenders is the identification of recommendations which are similar to the user’s requirements. Especially, in cases where the user requirements are inconsistent with the underlying constraint set, constraint-based recommender systems have to identify and apply the most suitable diagnosis in order to identify a recommendation and to increase the user’s satisfaction with the recommendation. Given this motivation, we developed two different approaches which provide similar recommendations to users based on their requirements even when the user’s preferences are inconsistent with the underlying constraint set. We tested our approaches with two real-world datasets and evaluated them with respect to the runtime performance and the degree of similarity between the original requirements and the identified recommendation. The results of our evaluation show that both approaches are able to identify recommendations of similar solutions in a highly efficient manner.
KW - decision support systems
KW - constraint-based recommender systems
KW - similarity measures
KW - recommendation similarity
UR - https://link.springer.com/chapter/10.1007/978-3-030-22999-3_26
U2 - https://doi.org/10.1007/978-3-030-22999-3_26
DO - https://doi.org/10.1007/978-3-030-22999-3_26
M3 - Conference paper
SN - 978-3-030-22998-6
T3 - Lecture Notes in Computer Science
SP - 287
EP - 299
BT - Advances and Trends in Artificial Intelligence
PB - Springer
CY - Cham
T2 - 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems
Y2 - 9 July 2019 through 11 July 2019
ER -