correlation-sensitive next-basket recommendation · datasets:tafeng(e-commerce); and...

Datasets: TaFeng (E-commerce); and Delicious (Bookmark Tag)

Recommendation: !"#$% ← {(|r+(-) ≤ 0}, where 2+

(-) is the ranking of item (; and 2(-): 4(-) → 1, 2, … , :Methodology: For a given testing basket sequence ;, hide last basket ! and generate the next-basket recommendation given < ;\B >Metric: Half-life Utility (HLU) measures the overall ranking performance. Higher is better.

Conclusion: Experiments on the two datasets show that the modeling of correlation information contributes statistically significant improvements as compared to traditional basket-sequence models in terms of top-K recommendations.

Basket Sequence Correlation Networks (Beacon)

Correlation-Sensitive Next-Basket RecommendationDuc-Trong Le, Hady W. Lauw, Yuan Fang

Problem

Experiments

Item-Item Correlation Matrix

v Input: a set of items V; A ∈ ℝ|D|× D ; each basket BF → GF ∈ {0, 1}|D|,

Salmon, Wasabi,Japanese Rice

Crab, Pepper,Melted Butter, Garlic

Fresh Oyster,Fresh Milk, Wasabi

Fresh Oyster,Lemon, Mint

Leaf

Independent

Recommendation

Correlation-Sensitive RecommendationT=1 T=2 T=3

?

Task: Modeling concurrentlyv Correlative associations among items of a basketv Sequential associations across baskets of a sequence

to predict the next basket of correlated items.

Motivating example:

Food RecommendationCount

Co-occurrence 0 0 2 10 0 0 12 0 0 11 1 1 0

Co-occurrence Matrix !

Correlation Matrix "

{Milk, Eggs, Bread}

{Jam, Bread}{Milk, Eggs}

0 0 .67 .33

0 0 0 .58.67 0 0 .33.33 .58 .33 0

Normalize

Objective: Leverage correlations between item-item pairs

Properties:

v A pair with frequent co-occurrence has a higher score than lessfrequent ones.

v A pair with exclusive connection has a higher score than non-exclusive ones.

1 0 1 1

!0

Correlation-Sensitive Basket Encoder

… 0 1 0 1

B

…

LSTM LSTM…

…!ℓ(%)

Sequence Encoder

'0 'ℓ(%)

(0 (ℓ(%)

)ℓ(%)

Basket Sequence S

Correlation-Sensitive Score Predictor

Item Scores *(%)

Correlation Matrix +

0 0 .67 .33

0 0 0 .58

.67 0 0 .33

.33 .58 .33 0

,- .

/.

# Module Operations Parameters

1

Corr

elat

ion-

Sens

itive

Ba

sket

Enc

oder

• The immediate representation IF ∈ ℝ D of BF:

JF = G% ∘ M + ReLU(G%A − TU)

• The L-dimensional latent representation V% ∈ ℝW of BF:

XF = ReLU IFY + Z

• Item importance M ∈ ℝ D

• Noise-cancelling T ∈ ℝ[

• Y ∈ ℝ|D|×\, Z ∈ ℝ\

2

Sequ

ence

En

code

r • The H-dimensional recurrent hidden output ]% ∈ ℝ^:

]F = tanh VFc + ]Fdecf + g

• c ∈ ℝW×^,g ∈ ℝ^

• c′ ∈ ℝ^×^

3

Corr

elat

ion-

Sens

itive

Sc

ore

Pred

icto

r • The sequential signal for next-item adoptions i(-) ∈ ℝ|D|:

i(-) = j ]ℓ(l)m• The correlation-sensitive score 4(-) ∈ ℝ D :

4(-) = n i - ∘ M + i - A + (1 − n)i -

• m ∈ ℝ^×|D|

n ∈ [0,1]

L=8 L=32L=8

L=32

L=32, H=16 L=64, H=32

L=64L=32

L=8,H=64 L=H=64

4.55.05.56.06.57.07.58.0

TaFeng Delicious

HLU

POPMCMCNDREAMBSEQtriple2vecBeacon

Target BookmarkDelicious Tag Basket Prediction (K=5)

Beacon MC POP

Manual de jQuery (1)

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The $300 Million Button (2)

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(1) https://desarrolloweb.com/manuales/manual-jquery.html (2) https://articles.uie.com/three_hund_million_button