User Modelling in I-Help: What, Why, When and How
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Homophily in online dating: when do you like someone like yourself?
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Network growth and the spectral evolution model
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
The link prediction problem in bipartite networks
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Reciprocal and heterogeneous link prediction in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
What is the added value of negative links in online social networks?
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.00 |
A typical recommender setting is based on two kinds of relations: similarity between users (or between objects) and the taste of users towards certain objects. In environments such as online dating websites, these two relations are difficult to separate, as the users can be similar to each other, but also have preferences towards other users, i.e., rate other users. In this paper, we present a novel and unified way to model this duality of the relations by using split-complex numbers, a number system related to the complex numbers that is used in mathematics, physics and other fields. We show that this unified representation is capable of modeling both notions of relations between users in a joint expression and apply it for recommending potential partners. In experiments with the Czech dating website Libimseti.cz we show that our modeling approach leads to an improvement over baseline recommendation methods in this scenario.