FOLLOW THE SLIDES HERE
https://slides.com/kirillkasjanov/recommender-systems
E-Commerce Recommender Systems
TWO-TOWER DEEP NEURAL NETWORK APPLICATION
FOR CANDIDATE RETRIEVAL
INTRODUCTION
- Recommender Systems are one of the main application of ML/DL in B2C Businesses
- Learning from user experience and interactions is the foundation
- Generate constantly quality recommendations is difficult and often impossible
- Big enterprise (Amazon, Netflix, Google etc.) invest and develop but still cannot perfect them
Challenges in Model Design
- No "Ground Truth"
- Changing Environment
- Manage adverse Effects
Recommend items that are related to what the customer likes.
Collaborative Filtering
Recommend items also based on customer similarity.
Content-based Filtering
Recommends items based on multidimensional similarity of both customers and products
Matrix Factorization
Main Approaches
How it works?
| X | ||
|---|---|---|
| X | ||
| X |
| X |
|---|
Rock
Jazz
Pop
| X | ||
|---|---|---|
| X | ||
| X |
Metallica
Taylor Swift
Miles Davis
| X |
|---|
Content-Based Filtering
Collaborative Filtering
Metallica
Taylor Swift
Miles Davis
Energetic
Calm
Emotional lyrics
No Lyrics
Madonna
+1
-1
+1
-1
How it works?
| 0.9 | 0.3 | -0.8 | 0.5 |
| 0.2 | 0.8 | -0.8 | 0.3 |
| -0.4 | -0.3 | 0.5 | -0.3 |
| -0.7 | 0.1 | 0.3 | -0.3 |
Metallica
Taylor Swift
Miles Davis
Madonna
| 0.9 | 0.2 | -0.7 | 0.5 |
|---|---|---|---|
| 0.2 | 0.9 | -0.9 | 0.3 |
Energetic - Calm
Emotional Lyrics - Instrumental
| 1.0 | -0.1 |
|---|
| 0.0 | 0.9 |
|---|
| -0.4 | -0.3 |
|---|
| -0.9 | 0.3 |
|---|
| x | |||
|---|---|---|---|
| x | |||
| x | |||
| x |
Metallica
Taylor Swift
Miles Davis
Madonna
| 0.9 | 0.2 | -0.7 | 0.5 |
|---|---|---|---|
| 0.2 | 0.9 | -0.9 | 0.3 |
Energetic - Calm
Emotional Lyrics - Instrumental
| 1.0 | -0.1 |
|---|
| 0.0 | 0.9 |
|---|
| -0.4 | -0.3 |
|---|
| -0.9 | 0.3 |
|---|
Generate Recommendations with Matrix Factorization
Two-Tower DNN
Extending MF to Deep Learning
Candidate Tower
Query Tower
Xinyang et al. 2019
0.4
0.3
0.5
0.8
0.6
(IN-BATCH) SOFTMAX
LOSS
Hidden Layers
EMBEDDINGS
Customer Query
Retrieval
Embedding Computation
Embedding Space Lookup
Request Recommendations
Retrieving non-linear candidate computations as recommendations
Portfolio Project
Recommender System for Beauty Care Products
Context and Data
- Beauty Care Products 6.7k
- Customer: 250k
- Timestamped Full Year Data
Customer:
- Anonymous ID
- Timestamps
- Interactions
Product:
- Brand, Series
- Category, Gender
- Price, Vol
- Ingredients, Effects, Scent, Hairtype, Finish, Skintype
+ Images
Embedding Size = 32
1 Hidden Layer as Output
Embedding Size = 32
1 Hidden Layer as Output
Embedding Size = 32
1 Hidden Layer as Output
Adding Hidden Layers
(non-linear)
Larger dense layers non-linear activations impair learning...
Adding Hidden Layers
(non-linear)
Customer Actions: Categorical Encoding
Product Category: Categorical Encoding
Product Type: Categorial Encoding
Product Gender: Categorical Encoding
Special Product Features: One-Hot Encoding
TRANSFORM TO
EMBEDDING
Adding Hidden Layers
+ Add More Embeddings
Generating Recommendations
Go to Demo Notebook- Approach does not require to have prior knowledge of the domain to be applied
- Encoded all variables in embeddings (exc. numerical) show to yield best results
- Linearity can be prevailing over non-linear activations. Try both setups to see what works best.
Takeaways and Insights
- More data on customer to enrich the query tower
- Explore more methodology like Rating vs. Retrieval and DCN
- Experiment more with CNN for product images to identify more features
Future Work
THANK YOU!
