Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2365934.2365942 | Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web |
Keywords | Field | DocType |
typical recommender setting,split-complex number approach,baseline recommendation method,potential partner,recommender system,split-complex number,number system,unified representation,complex number,joint expression,certain object,modeling approach | Recommender system,Czech,World Wide Web,Complex number,Computer science,Duality (optimization),Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
13 | 0.67 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Kunegis | 1 | 874 | 51.20 |
| Gerd Gröner | 2 | 171 | 14.70 |
| Thomas Gottron | 3 | 432 | 35.32 |