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