Item Recommendations Benchmark Task: Difference between revisions
Jump to navigation
Jump to search
(ContinuousReplacement) Tag: continuous replacement |
No edit summary |
||
| Line 1: | Line 1: | ||
An [[Item Recommendations Benchmark Task]] is a [[benchmark prediction task]] that is an [[item(s) recommendation task]]. | An [[Item Recommendations Benchmark Task]] is a [[benchmark prediction task]] that is an [[item(s) recommendation task]]. | ||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** [[Netflix Prize | ** [[Netflix Prize Benchmark]]. | ||
** [[MovieLens Benchmark]]. | |||
* <B>See:</B> [[KONNECT]], [[Item Relevance Ranking]]. | * <B>See:</B> [[KONNECT]], [[Item Relevance Ranking]]. | ||
---- | ---- | ||
Revision as of 18:49, 12 February 2020
An Item Recommendations Benchmark Task is a benchmark prediction task that is an item(s) recommendation task.
- Example(s):
- See: KONNECT, Item Relevance Ranking.
References
2018
- (Ying et al., 2018) ⇒ [[::Rex Ying]], [[::Ruining He]], [[::Kaifeng Chen]], [[::Pong Eksombatchai]], [[::William L Hamilton]], and [[::Jure Leskovec]]. ([[::2018]]). “Graph Convolutional Neural Networks for Web-scale Recommender Systems.” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining.
- QUOTE: ... Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. ...