Thema5127: Unterschied zwischen den Versionen
Mp3616 (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Abschlussarbeit |Titel=ETARS: An Explainable Time-Aware GNN-based Recommender System |Vorname=Zhan |Nachname=Qu |Abschlussarbeitstyp=Bachelor, Master |Betreu…“) |
Mp3616 (Diskussion | Beiträge) |
||
| Zeile 8: | Zeile 8: | ||
|Abschlussarbeitsstatus=Offen | |Abschlussarbeitsstatus=Offen | ||
|Beginn=2024/01/16 | |Beginn=2024/01/16 | ||
| + | |Ausschreibung=Thesis_ExplainableRecommendations.pdf | ||
|Beschreibung DE=In recent years, recommendation systems have become indispensable in our daily lives, facilitating users in discovering new content and products across diverse domains. How- ever, there exists a growing demand for recommendation systems that not only deliver accurate suggestions but also elucidate the rationale behind their recommendations. Moreover, while many recommendation systems concentrate on static interactions, real- world user engagements dynamically evolve over time. | |Beschreibung DE=In recent years, recommendation systems have become indispensable in our daily lives, facilitating users in discovering new content and products across diverse domains. How- ever, there exists a growing demand for recommendation systems that not only deliver accurate suggestions but also elucidate the rationale behind their recommendations. Moreover, while many recommendation systems concentrate on static interactions, real- world user engagements dynamically evolve over time. | ||
Aktuelle Version vom 16. Januar 2024, 12:30 Uhr
Abschlussarbeitstyp: Bachelor, Master
Betreuer: Zhan Qu
Forschungsgruppe: Web Science
Archivierungsnummer: 5127
Abschlussarbeitsstatus: Offen
Beginn:
16. Januar 2024
Abgabe: unbekannt
In recent years, recommendation systems have become indispensable in our daily lives, facilitating users in discovering new content and products across diverse domains. How- ever, there exists a growing demand for recommendation systems that not only deliver accurate suggestions but also elucidate the rationale behind their recommendations. Moreover, while many recommendation systems concentrate on static interactions, real- world user engagements dynamically evolve over time.
This thesis endeavors to address these challenges by proposing an innovative counter- factual explanation method tailored for time-aware Graph Neural Network (GNN)-based recommender systems. Engaging in this research will provide an opportunity to gain profound insights into leveraging cutting-edge explainable Artificial Intelligence (AI) tech- niques for interpreting temporal graph neural networks. Furthermore, it offers a chance to comprehend the operational intricacies of recommender systems.
[1] https://www.mdpi.com/2076-3417/13/20/11176
Ausschreibung: Download (pdf)