Xing Zhao, Qingquan Song, James Caverlee and Xia HuDepartment of Computer Science and Engineering

Texas A&M University, USA

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Dataset Statistics

Items Quantity Proportion

Playlists 1,000,000

Unique Tracks 2,262,292 100%

Unique tracks (freq ≥ 5) 599,341 96.05% Unique tracks (freq ≥ 100) 70,229 80.67%

Unique albums 734,684Unique artists 295,860

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Track Appeared Times in Training Data1 5 10 100 1000 10000 40000

Num

ber o

f Rem

aini

ng T

rack

s

#106

0

0.5

1

1.5

2

2.5

Cum

sum

Tak

ing

Up

of P

ositi

ve S

ampl

es

0

0.2

0.4

0.6

0.8

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Therefore, in some part of our methods, weonly consider these tracks for training.

Our Method - TrailMix

DNCF

C-Tree

CC-Title

5

PlaylistContinuation:For Task 2 to

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Cold Start: ForTask 1

3 7

5 21

3 43

6 81

8 32

7

13 14

6 5

Tracks (2,262,292)

Words

(9,817)

Word list 1:Track list 1

Word list 2:Track list 2

Word list 3:Track list 3

…

…

…Clu

ster

Recommend

New title: e.g. Pop Punk 2018 Summer

Wordlist Tracks

Word list Tracks

Word list Tracks

Word list Tracks

Word list Tracks

Normalize

Pre-process

…

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CC-Title: Context Clustering using Title

i

j

Track i is existed in 6playlists whose titlecontain word j

Items Quantityunique titles 92,944

unique normalized titles 17,381 unique non-stop

normalized words 9,817

playlist without title after processing 22,921

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Steps:1. Preprocessing: stemming, stop words,

emoji, punctuation, etc.2. Building word-track matrix of size

9817 x 2,262,2923. Normalizing cells using ‘IDF’4. Clustering words based on row

similarity5. Recommend tracks in each cluster for

new title

CC-Title: Cont.

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Highlight:1. CC-Title could deal with large scale of matrix

computation with high efficiency.2. In some cases (clusters), the performance is

very good.

CC-Title: Cont.

Pros: 1. Simple and Generic 2. Ensemble the advantages of basic matrix factorization model and MLP.

Cons:Computationally not efficient tobe directly applied on the targetproblem due to the huge itemscope and the matrix sparsity.

DNCF: Decorated Neural Collaborative Filtering

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He et al. , “Neural Collaborative Filtering”. WWW, 2017.

Neural Collaborative Filtering

DNCF: Cont.

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Two modifications to address efficiency issue:

Training Phase: Constrained Negative Sampling.

Testing Phase: Constrained Recommendation with Reordering.

2. Positive samples remain the wholedataset during training to protect thefeasible embedding and prediction ofall the testing data. (Task 2-10)

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1. Constrain the negative samplingspace to the space of the tracksappearing equal to or more than 100times in the training data.

Track Appeared Times in Training Data1 5 10 100 1000 10000 40000

Num

ber o

f Rem

aini

ng T

rack

s

#106

0

0.5

1

1.5

2

2.5

Cum

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0.2

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Training Phase: Constrained Negative Sampling.

DNCF: Cont.

2. Reorder the predicted 500 tracks with an ensemble trick leveraging two types of predictions provided by the Word2Vec embedding.

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1. Constrain the recommendation space by only recommending the popular tracks (>=100 times) during testing phase towards a more targeted prediction.

Testing Phase: Constrained Recommendation with Reordering.

DNCF Word2Vec (1) Word2Vec (2)

L1 L2 L3

φ1 φ2 φ3

φ1 \ L1 ∪ L2 ∪ L3

DNCF: Cont.

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Highlight:1. Results steadily increase with maximum performance at seed 25;2. It performs better for playlists with random seeding tracks (R) than

sequential seeding tracks;

DNCF: Result

C-Tree: Constructed Tree

A Playlist is:1. Natural tree-structure: A playlist

consists of different tracks ,andthese tracks always belong to a specific album of an artist;

2. Meaningful Cluster: A list of tracks in a specific playlist always have latent similarity, such as genres, style, listening sense, etc.

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Phylogenetic Tree.(Source: https://www.creative-biostructure.com/custom-

phylogenetic-tree-construction-service-399.htm)

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A Real Example (PID: 11548):• Playlist Title: Pop Puck• 48 tracks belongs to 12albums by 5 artists (2 rockbands and 3 pop punkbands)

Pop punk band

Rock bandHow do we compare theinternal relationship?How do we compare itwith another tree(external)?

C-Tree: Cont.

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Training Data: Complete Tree Testing Data: Incomplete Tree

External comparisonIncomplete Tree: A playlistonly contains partial oftracks (seed), which is

waiting for recommending.

C-Tree: Cont.

Steps:

1. Building Forest: 1 millioncomplete trees;

2. Comparing and normalizing thedistance between theincomplete tree T-test andcomplete tree T-train;

3. Recommending the tracks(leaves) from each T-train to theincomplete tree T-test, based onthe score of each leaf.

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Playlist 1

Playlist 2

Playlist 3

Playlist 4

Playlist n

…

C-Tree: Cont.

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C-Tree: Result

Highlight:1. Results steadily increase with maximum performance at seed 25;2. It performs better for playlists with random seeding tracks (R) than

sequential seeding tracks;

TrailMix: Ensemble Model

CC-Title

FinalRecommendation

ADNCF BDNCF

AC-Tree BC-Tree

Num_handout

Metho

d1

Metho

d2

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Experiment and Result

Experiment Setting:• Training 80%, testing 20%: cross-validation for hyperparameter tuning• Testing data strictly follows the rules designed byRecSys 2018

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Thank you!

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Q&A

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