Fast incremental matrix factorization for recommendation with positive-only feedback

João Vinagre1,2

Alípio Jorge1,2

João Gama1,3

1 LIAAD - INESC TEC, Porto, Portugal2 Faculdade de Ciências – Universidade do Porto3 Faculdade de Economia – Univesidade do Porto

UMAP 2014July 7-11Aalborg, Denmark

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Outline

1. Introduction

2. Incremental matrix factorization

3. Evaluation protocol

4. Experiments and results

5. Conclusions and future work

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1. Introduction

● Contributions:● Incremental matrix factorization algorithm

– for positive-only feedback (aka binary)

– online learning with SGD

● Online evaluation protocol

– online monitoring

– evaluation over time

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2. Incremental MF: motivation

● Q: Why incremental?● Recommendation algorithms deal with ever growing user

feedback:

– continuous data fow

– variable rate

– unpredictable order● User behavior is naturally complex

– preference drifts / shifts

– moods

● A: Typicall data stream mining problem

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2. Incremental MF: motivation

● Q: Why use positive-only feedback?

● ratings data (explicit):

● positive-only feedback (typically implicit)

– like button

– web access logs

– music listening / playlisting

– shopping history

– news reading

– event participation

– …● A: Widely available and less intrusive (both for user and for system)

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2. Matrix factorization – Batch SGD

≈ .

● Training: minimize prediction error for known ratings → stochastic gradient descent (SGD)

● Process is iterative (several passes through dataset)

R̂ui=Au⋅BiT

R A B

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2. Matrix Factorization: Incremental SGD

● Positive-only feedback: assume Rui = 1 for every observed (u,i)

● Incremental learning:

● for each newly observed (u,i) Au and Bi are adjusted

● only one pass is performed (i.e. one iteration)

● Recommendation:

● sort items i by descending |1 – Ȓui| for each user u

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3. Evaluation protocol

● Traditional train-test-holdout → stationary data

● Our approach: prequential evaluation

for each (u,i):

1 – recommend items to u (if u is known)

2 – score recommendation, given the observed i

3 – update the model with (u,i)

● Continuous monitoring

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4. Experiments and results: algorithms and data

● Algorithms:

● UserKNN: Incremental user-based neighborhood (classic reference);

● (W)BPRMF: (Weighted) Bayesian Personalized Ranking Matrix Factorization (Rendle et al. 2009);

● ISGD

● Datasets:

Dataset Events # Users # Items Time frame Sparsity

Lastfm-600k 493.063 164 65.013 8 months 99,11%

Music-playlist 111.942 10.392 26.117 45 months 99,96%

Music-listen 335.731 4.768 15.323 12 months 99,90%

Movielens 226.310 6.014 3.232 34 months 98,84%

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4. Experiments and results: overall results

Dataset Algorith Recall@10 Update time

Lastfm-600k

BPRMF 0.003 28.061

WBPRMF 0.003 29.194

ISGD 0.034 1.106

UKNN 0.006 290.133

Music-playlist

BPRMF 0.020 1.889

WBPRMF 0.057 2.156

ISGD 0.171 0.949

UKNN 0.132 190.250

Music-listen

BPRMF 0.028 0.846

WBPRMF 0.056 1.187

ISGD 0.061 0.118

UKNN 0.139 328.917

Movielens-1M

BPRMF 0.080 0.173

WBPRMF 0.084 0.229

ISGD 0.050 0.016

UKNN 0.110 84.927

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4. Experiments and results: evolving results

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4. Experiments and results: evolving results

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5. Conclusions

● Algorithm:● ISGD is clearly faster than tested alternatives;

● accuracy of ISGD is competitive, but not a clear winner.

● Evaluation protocol● allows a fne-grained assessment of results, by

continuously monitoring the learning process.

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5. Future work

● Deal with variability in results:

● assess the sensitivity of algorithms to data meta-features

– domain

– sparseness

– user-item ratio

– user behaviour (in)consistency

– noise, shilling attacks, etc● Verify convergence between incremental and batch

● Deal with temporal effects:

● forgetting

● time-awareness

● drift/shift detection → automatic parameter tuning

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