ModelMate: A recommender for textual modeling languages based on pre-trained language models
Abstract
References
Index Terms
- ModelMate: A recommender for textual modeling languages based on pre-trained language models
Recommendations
Domain-specific textual meta-modelling languages for model driven engineering
ECMFA'12: Proceedings of the 8th European conference on Modelling Foundations and ApplicationsDomain-specific modelling languages are normally defined through general-purpose meta-modelling languages like the MOF. While this is satisfactory for many Model-Driven Engineering (MDE) projects, several researchers have identified the need for domain-...
Model-driven engineering with domain-specific meta-modelling languages
Domain-specific modelling languages are normally defined through general-purpose meta-modelling languages like the MOF. While this is satisfactory for many model-driven engineering (MDE) projects, several researchers have identified the need for domain-...
Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsTraditional recommender systems leverage users’ item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to express language-based preferences offer a fundamentally different modality for ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Conferences](/cms/asset/24bf6cca-1495-4f6d-a361-504c834207f6/3640310.cover.jpg)
- General Chairs:
- Alexander Egyed,
- Manuel Wimmer,
- Program Chairs:
- Marsha Chechik,
- Benoit Combemale
Sponsors
- Johannes Kepler University, Linz, Austria
- SIGSOFT: ACM Special Interest Group on Software Engineering
- IEEE CS
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Badges
Best Paper
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 123Total Downloads
- Downloads (Last 12 months)123
- Downloads (Last 6 weeks)12
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in