The document outlines a project focused on enhancing product title quality in e-commerce, detailing key challenges in clarity and conciseness. It includes processes for data cleansing, feature engineering, and model evaluation, employing various machine learning techniques. The collaboration involves multiple experts from academia and industry, aimed at developing an efficient classifier for product attribute recognition.

1. Photo By: David Doubilet

2. CIKM AnalytiCup
Lazada Product Title Quality Challenge
1
$6,000
2$2,000
3$1,000
$2,000

3. Team Members
Tam T. Nguyen
nthanhtam@gmail.com
Postdoctoral Research Fellow
Ryerson University
Kaggle Grandmaster
Hossein Fani
hosseinfani@gmail.com
PhD Student
University of New Brunswick
Gilberto Titericz
giba1978@gmail.com
Machine Learning Expert
AirBnb Inc.
Kaggle Grandmaster
Ebrahim Bagheri
ebrahim.bagheri@gmail.com
Associate Professor
Ryerson University

4. Photo By: Justin Hofman

5. “hot sexy red clutch rug sack travel backpack unisex cheap with free gift”
𝑦1
clarity
𝑦2
conciseness
“Hot Sexy Tom Clovers Womens Mens Classy Look Cool Simple Style Casual
Canvas Crossbody Messenger Bag Handbag Fashion Bag Tote Handbag Gray”
Problem Setting

6. Photo By: David Doubilet

7. Clarity if within five seconds one can understand the title, what the product is, and quickly figure out the key
attributes (color, size, model, ...).
Conciseness if it is short enough to contain all the necessary information. Otherwise, i.e., the title is
too long with many unnecessary words, Or it is too short such that it is unsure what the product is.
Data Set

9. ML-DM
1. Cleansing
• Noise
• Missing Values
• Outliers
2. Flirting
• Attributes
• Labels (if any)
• Augmentation
3. Feature Eng.
• Extraction
• Reduction
• Selection
4. Model Eng.
• Selection
• Tuning
• Evaluation

10. 1. Cleansing
• Noise
• Html tags in ‘short_description’ (%94)
• Missing Values
• ‘product_type’ (less than %1)
• ‘category_lvl_3’ (about %6) → assign ‘category_lvl_2’
• ‘description’ (less than %1)
• Outliers
• ‘price’ {-1, 999999, 9999999},
• ‘price’ Normalization based on country

11. 2. Flirting
• Attributes
• Color
• Brand
• Non-English
• <img> Image
• <li> enumeration
• 𝒚: Labels
• Disagreement in labels!(label noise)
• Augmentation
• Cloning  color, brand

12. Label Noise

14. multi-class
𝑓: 𝑋1 × 𝑋2 × … × 𝑋 𝑑 → 𝑦: 𝑐1, 𝑐2, … , 𝑐 𝑘
binary(boolean) classifier: 𝑦: 0,1
multi-output(label)
𝑓: 𝑋1 × 𝑋2 × … × 𝑋 𝑑 → 𝑦1: 𝑐1, 𝑐2, … , 𝑐 𝑘1
× 𝑦2: 𝑐1, 𝑐2, … , 𝑐 𝑘2
× ⋯ × 𝑦𝑟: 𝑐1, 𝑐2, … , 𝑐 𝑘r
multi-output binary(boolean) classifier: 𝑦1: 0,1 × 𝑦2: 0,1
Targets correlation: (single, fast model for all targets)
Only 3 combinations for (Clear,Concise):
(1,0), (1,1), (0,0)  |~Clear & Concise|= 0
if ~Clear then ~Concise
if Concise then Clear

16. 3. Feature Eng.
• Extraction
• Reduction
• LSA,T-SNE,PCA,SVD
• Selection
• STD
• Correlation X~y
• Linear(t-test, chi2)
• Non-linear(mi)
• Model-driven
• LinearSVM
Feature Engineering

17. Feature Importance
Linear SVM

19. 10-Fold Set 1 10-Fold Set 2 10-Fold Set 3 10-Fold Set 4
Base Model
Ensemble Model
Final Prediction
Fold Bagging
Fold Bagging
Set Fold Bagging
BLENDBLEND BLEND BLENDSTACK STACK STACK STACK
BLENDBLEND BLEND BLEND
BLEND
Bagging Models

20. Performance Evaluation
SGD: stochastic gradient descent
LOR: logistic regression
RDG: ridge regression
NBC: naive bayes classifier
XGB: extreme gradient boosting
LGB: light gradient boosting
W2V: word2vec

21. Model Importance
clarity conciseness