Pith Number

pith:PII5Q5WV

pith:2012:PII5Q5WV6SLT4E5T25LCZRGA2A

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BPR: Bayesian Personalized Ranking from Implicit Feedback

Christoph Freudenthaler, Lars Schmidt-Thieme, Steffen Rendle, Zeno Gantner

Bayesian Personalized Ranking derives an optimization criterion that directly targets ranking from implicit feedback.

arxiv:1205.2618 v1 · 2012-05-09 · cs.IR · cs.LG · stat.ML

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Claims

C1strongest claim

Our experiments indicate that for the task of personalized ranking our optimization method outperforms the standard learning techniques for MF and kNN.

C2weakest assumption

The assumption that maximizing the BPR-Opt posterior (derived from a Bayesian analysis of pairwise preferences) produces better ranking performance than standard losses when applied to matrix factorization and kNN models.

C3one line summary

BPR-Opt is a maximum posterior estimator for personalized ranking from implicit feedback that, when used to train matrix factorization and adaptive kNN, outperforms standard learning techniques on ranking tasks.

References

15 extracted · 15 resolved · 0 Pith anchors

[1] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learn- ing to rank using gradient descent. In ICML ’05: Proceedings of the 22nd international con- ference on Mach 2005

[2] M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems. Springer-Verlag , 22/1, 2004 2004

[3] A. Herschtal and B. Raskutti. Optimising area under the roc curve using gradient descent. In ICML ’04: Proceedings of the twenty-ﬁrst inter- national conference on Machine learning, page 49, New York, 2004

[4] T. Hofmann. Latent semantic models for collabo- rative ﬁltering. ACM Trans. Inf. Syst. , 22(1):89– 115, 2004 2004

[5] Y. Hu, Y. Koren, and C. Volinsky. Collaborative ﬁltering for implicit feedback datasets. In IEEE International Conference on Data Mining (ICDM 2008), pages 263–272, 2008 2008

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Cited by

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Receipt and verification

First computed	2026-05-18T03:55:47.886750Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7a11d876d5f4973e13b3d7562cc4c0d024ea0678b23167d1feb3501d273c707a

Aliases

arxiv: 1205.2618 · arxiv_version: 1205.2618v1 · doi: 10.48550/arxiv.1205.2618 · pith_short_12: PII5Q5WV6SLT · pith_short_16: PII5Q5WV6SLT4E5T · pith_short_8: PII5Q5WV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PII5Q5WV6SLT4E5T25LCZRGA2A \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7a11d876d5f4973e13b3d7562cc4c0d024ea0678b23167d1feb3501d273c707a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "892b0d523a721ff71fc0c2bb6c9327782bb694b8845a702c7f431065a4578439",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2012-05-09T18:25:09Z",
    "title_canon_sha256": "a2ae5ee8235f28fc19ef50476f72eb3c83c25f5b36bb5546e91b38660f177512"
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  "source": {
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    "kind": "arxiv",
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