TY  - CONF
AU  - Alejandro Ariza-Casabona
AU  - Bartlomiej Twardowski
AU  - Tri Kurniawan Wijaya
A2  - ECIR
PY  - 2023//
TI  - Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation
T2  - LNCS
BT  - European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval
SP  - 49–65
VL  - 13980
N2  - Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method.
UR  - https://link.springer.com/chapter/10.1007/978-3-031-28244-7_4
N1  - LAMP
ID  - Alejandro Ariza-Casabona2023
ER  -