Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017

Personalized User Recommendations at Tinder: The TinVec Approach:
With 26 million matches per day and more than 20 billion matches made to date, Tinder is the world’s most popular app for meeting new people. Our users swipe for a variety of purposes, like dating to find love, expanding social networks and meeting locals when traveling.
Recommendation is an important service behind-the-scenes at Tinder, and a good recommendation system needs to be personalized to meet an individual user’s preferences. In this talk, we will discuss a new personalized recommendation approach being developed at Tinder, called TinVec. TinVec embeds users’ preferences into vectors leveraging on the large amount of swipes by Tinder users. We will discuss the design, implementation, and evaluation of TinVec as well as its application to
personalized recommendations.

Bio: Dr. Steve Liu is chief scientist at Tinder. In his role, he leads research innovation and applies novel technologies to new product developments.

He is currently a professor and William Dawson Scholar at McGill University School of Computer Science. He has also served as a visiting research scientist at HP Labs. Dr. Liu has published more than 280 research papers in peer-reviewed international journals and conference proceedings. He has also authored and co-authored several books. Over the course of his career, his research has focused on big data, machine learning/AI, computing systems and networking, Internet of Things, and more. His research has been referenced in articles publishing across The New York Times, IDG/Computer World, The Register, Business Insider, Huffington Post, CBC, NewScientist, MIT Technology Review, McGill Daily and others. He is a recipient of the Outstanding Young Canadian Computer Science Researcher Prizes from the Canadian Association of Computer Science and is a recipient of the Tomlinson Scientist Award from McGill University.

He is serving or has served on the editorial boards of ACM Transactions on Cyber-Physical Systems (TCPS), IEEE/ACM Transactions on Networking (ToN), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Vehicular Technology (TVT), and IEEE Communications Surveys and Tutorials (COMST). He has also served on the organizing committees of more than 38 major international conferences and workshops.

Dr. Liu received his Ph.D. in Computer Science with multiple honors from the University of Illinois at Urbana-Champaign. He received his Master’s degree in Automation and BSc degree in Mathematics from Tsinghua University.

  • Identifiez-vous pour voir les commentaires

Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017

  1. 1. Personalized Recommendations at The TinVec Approach Steve Liu Chief Scientist
  2. 2. 190+ countries 40+ languages 1.6B+ swipes daily 20B+ matches Tinder on a Global Scale
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. TinVec
  7. 7. 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
  8. 8. 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
  9. 9. Swipers Ashley Alex Bob Charlie David Swipees Josh Bernadette Caitlin Sarah
  10. 10. Skip-gram for Tinder Sentence: Co-swipes: (likes) CharlieBobAlex
  11. 11. Skip-gram for Tinder (cont’d) Context Context Target Context: Alex Bob Charlie
  12. 12. How to Obtain The User Embeddings INPUT PROJECTION OUTPUT Target: Bob Context: Alex & Charlie
  13. 13. 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)
  14. 14. Similar Swipees are Clustered Together
  15. 15. 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
  16. 16. 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
  17. 17. Application of TinVec
  18. 18. 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
  19. 19. 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
  20. 20. Tinder Science and the Tinder Team