Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017
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Transcript of Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017
Personalized Recommendations at
The TinVec Approach
Steve LiuChief Scientist
190+countries
40+languages
1.6B+swipes daily
20B+matches
Tinder on a Global Scale
Overview
● Personalized Recommendations and why they matter ● The TinVec approach
○ Why choose + how to obtain user embedding?○ How to leverage user embedding to provide match
recommendations? ○ Samples from TinVec results
● Evaluation● Conclusion and Future Product Implementation
Personalized Recommendations
● Today we have personalized experiences using social networks , eCommerce platforms or entertainment services
● Goal: to improve Tinder user’s experience○ Each user has his/her own tastes (like, pass)○ Personalized recommendations => users seeing relevant profiles○ Better user experience: increased and improved matches and messages
Personalized Recommendations at Tinder
● Collaborative filtering
● Content-based filtering
○ Natural Language Processing - Bios
● TinVec○ Utilizes swipe information○ Users are represented as vectors in an embedding
space○ Neural-network-based approach
TinVec
TinVec Mechanics
● Users: swipers and swipees● Each swipee is mapped to a vector
○ Embedded vector in an embedding space● The embedded vector represents possible characteristics of
the swipee implicitly○ Activities: playing football, surfing○ Interests: whether they like pets ○ Environment: outdoors vs. indoors○ Chosen career path: whether they are software
engineers or medical doctors● Close proximity of two embedded vectors indicates
○ The swipees are similar => share common characteristics● Goal: Recommendation
○ Identify more users whom you are likely to swipe right on
Sarah
TinVec and Word2Vec ● What is an embedding?
○ Vector representation of entities in the latent space○ “Similar” entities are mapped to nearby points
● Why?
○ Represent entities more efficiently (~Tens or hundreds v.s. ~millions)○ Useful for many tasks
■ NLP, recommendations■ You can do calculations on them!
Goal (output) Property Training Training data
Word2Vec (Mikolov et al., 2013)
Word embedding
Words share common contexts are closer in the vector space
Neural Networks
Large corpus of texts
TinVec User (Swipee) embedding
Swipees share common characteristics are closer in the vector space
Neural Networks
Large amount of co-swipes
Swipers
Ashley
Alex Bob Charlie David
Swipees
Josh Bernadette Caitlin
Sarah
Skip-gram for Tinder
Sentence:
Co-swipes:(likes)
CharlieBobAlex
Skip-gram for Tinder (cont’d)
Context Context
Target
Context:
Alex Bob Charlie
How to Obtain The User Embeddings
INPUT PROJECTION OUTPUT
Target: Bob Context: Alex & Charlie
Clusters in the Embedding Space
A point: A swipee’s embedded vector in the latent embedding space
Close proximity: Similar users (who are co-swiped by many swipers)
Similar Swipees are Clustered Together
How Do We Recommend from the Embedding Space?
Preference vector
1. Josh’s preference is represented by the mean embedded vectors of his likes
2. Users with close proximity to the preference vector will be recommended to him
Debbie
How Accurately Can You Predict a Swipe Left or Right?
● Area under ROC = 90%● F1 = 85%
TinVec
● Receiver Operating Characteristic Curve)
● TPR = Recall
● FPR
● Precision
#Correctly_Predicted_Likes
#Total_Real_Likes
#Incorrectly_Predicted_Likes
#Total_Real_Passes
#Correctly_Predicted_Likes
#Total_Predicted_Likes
Application of TinVec
TinVec + New Product Experiences
● Goal: ○ Use machine learning to present users that we are confident swipers will
like - in a fun, spontaneous and engaging way● Will roll out slowly first to maximize quality
Conclusion
● Personalized Recommendation matter at Tinder● TinVec: A new personalized recommendation approach
○ Based on the user embeddings○ Simple input data: only swipes (no user profile data)○ Training using neural networks
● Clusters show meaningful set of users that share common characteristics● Swipe prediction achieved high accuracy ● Serves as the foundation for building new user experiences at Tinder
Tinder Science and the Tinder Team