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Recommendation research benchmarks – Page 16 · SOTA2 Research

Recommendation

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

Multi-view Attention Fusion with Dynamic Behavioral Profiling of Heterogeneous Hypergraphs

48.14Precision@10

9

Mar 17, 2026

Assistment 2017

Multi-view Attention Fusion with Dynamic Behavioral Profiling of Heterogeneous Hypergraphs

83.75P@10

9

Mar 17, 2026

Assistment 2009

Multi-view Attention Fusion with Dynamic Behavioral Profiling of Heterogeneous Hypergraphs

45.41Precision@10

9

Mar 17, 2026

MovieLens dense

0.4NDCG@1

9

Mar 11, 2026

MovieLens sparse

29NDCG@1

9

Mar 11, 2026

Taobao-MM (test)

49.12HR@3

9

Feb 26, 2026

Industrial Dataset Taobao (test)

0.525HR@3

9

Feb 26, 2026

0.51V@5

9

Feb 26, 2026

0.333V@5

9

Feb 26, 2026

0.468V@5

9

Feb 26, 2026

MovieLens 32M Small Scale (90-10 random holdout)

9.03Preparation Time

9

Feb 26, 2026

1Delta Exp@50

9

Feb 26, 2026

1Delta Expected Rank @50

9

Feb 26, 2026

1Delta Exp@50

9

Feb 26, 2026

0.524NDCG@1

9

Feb 26, 2026

33NDCG@1

9

Feb 26, 2026

Musical Instruments

0.302NDCG@1

9

Feb 26, 2026

Electronic (OOD)

0.024Recall@20

9

Feb 26, 2026

Yelp N=20 (test)

36.2Hit Rate @1

9

Feb 26, 2026

MovieTV N=20 (test)

0.475Hit Rate @ 1 (H@1)

9

Feb 26, 2026

0.588H@1

9

Feb 26, 2026

Goodreads N=20 (test)

26.57H@1

9

Feb 26, 2026

Books N=20 (test)

42.36Hit Rate @ 1

9

Feb 26, 2026

0.031NDCG@20

9

Feb 26, 2026

Amazon Electronics (test)

FolkScope (Matched Knowledge Graph)

1.052RMSE

9

Feb 26, 2026

Page 16 of 29

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MovieLens Amazon Yelp Beauty Gowalla

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