ACM RecSys
E358243
ACM RecSys is the premier international conference dedicated to research and innovation in recommender systems.
All labels observed (2)
| Label | Occurrences |
|---|---|
| ACM Conference on Recommender Systems | 1 |
| ACM RecSys canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3450841 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ACM RecSys Context triple: [ACM conferences, include, ACM RecSys]
-
A.
SIGIR
SIGIR is a leading ACM Special Interest Group focused on advancing research and development in information retrieval and search technologies.
-
B.
ACM International Conference on Web Search and Data Mining
The ACM International Conference on Web Search and Data Mining (WSDM) is a leading annual computer science research conference focusing on web search, data mining, and related areas of information retrieval and machine learning.
-
C.
ACM Transactions on the Web
ACM Transactions on the Web is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research and developments related to web technologies and applications.
-
D.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
-
E.
ACM International Conference on Web Media
The ACM International Conference on Web Media is a scholarly conference focused on research and advances in web-based media technologies, systems, and applications.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ACM RecSys Target entity description: ACM RecSys is the premier international conference dedicated to research and innovation in recommender systems.
-
A.
SIGIR
SIGIR is a leading ACM Special Interest Group focused on advancing research and development in information retrieval and search technologies.
-
B.
ACM International Conference on Web Search and Data Mining
The ACM International Conference on Web Search and Data Mining (WSDM) is a leading annual computer science research conference focusing on web search, data mining, and related areas of information retrieval and machine learning.
-
C.
ACM Transactions on the Web
ACM Transactions on the Web is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research and developments related to web technologies and applications.
-
D.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
-
E.
ACM International Conference on Web Media
The ACM International Conference on Web Media is a scholarly conference focused on research and advances in web-based media technologies, systems, and applications.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
academic conference
ⓘ
computer science conference ⓘ recommender systems conference ⓘ |
| audience |
graduate students
ⓘ
industry professionals ⓘ practitioners ⓘ researchers ⓘ |
| describedAs | premier international conference on recommender systems ⓘ |
| field |
data mining
ⓘ
information retrieval ⓘ machine learning ⓘ recommender systems ⓘ |
| firstHeld | 2007 ⓘ |
| firstLocation |
Minneapolis
ⓘ
surface form:
Minneapolis, Minnesota, United States
|
| frequency | annual ⓘ |
| fullName |
ACM RecSys
self-linksurface differs
ⓘ
surface form:
ACM Conference on Recommender Systems
|
| hasComponent |
challenge track
ⓘ
doctoral symposium ⓘ industry track ⓘ posters ⓘ reproducibility track ⓘ research paper track ⓘ tutorials ⓘ workshops ⓘ |
| language | English ⓘ |
| organizer | Association for Computing Machinery ⓘ |
| scope |
algorithms for recommendation
ⓘ
evaluation of recommender systems ⓘ fairness and transparency in recommender systems ⓘ industrial applications of recommender systems ⓘ interactive and conversational recommendation ⓘ personalization ⓘ privacy and security in recommender systems ⓘ research in recommender systems ⓘ user experience in recommender systems ⓘ user modeling for recommendation ⓘ |
| sponsor |
SIGCHI
ⓘ
surface form:
ACM SIGCHI
SIGIR ⓘ
surface form:
ACM SIGIR
SIGKDD ⓘ
surface form:
ACM SIGKDD
|
| topic |
collaborative filtering
ⓘ
content-based recommendation ⓘ context-aware recommendation ⓘ deep learning for recommendation ⓘ evaluation metrics for recommender systems ⓘ online experimentation for recommender systems ⓘ session-based recommendation ⓘ user studies for recommender systems ⓘ |
| typicalMonth | September ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: ACM RecSys Description of subject: ACM RecSys is the premier international conference dedicated to research and innovation in recommender systems.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
ACM Conference on Recommender Systems