Spotify's Music Recommendations Lambda Architecture
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Spotify's Collaborative Filtering platform powers our Discover Page. In light of the fact, that we have been adding one new user every three seconds, it is paramount that we do things in real-time. We redesigned our recommendation system and added a Storm based real-time platform.
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Spotify's Music Recommendations Lambda Architecture
- 1. Spotify’s Music Recommendations Lambda Architecture Esh Kumar @eshvk Emily Samuels @emilymsa
- 2. Overview ‣ Why Lambda? ‣ Use Case: Discover Recommendations • Batch Architecture • Real-time Architecture • Challenges ‣ Future Work
- 3. Why Lambda? • 1 new user every 3 seconds. • Contextual, time based recs more & more important
- 5. The Discover Page Algorithmically generated fresh recs for users.
- 6. The Discover Batch Pipeline
- 7. Machine Learning Deep Dive
- 8. Word2Vec Words with similar contexts have similar meaning
- 9. Word2Vec King – Man + Woman = Queen
- 10. Annoy • Approximate Nearest Neighbors Oh Yeah! • https://github.com/s potify/annoy
- 12. Intro to Storm
- 13. Storm • Distributed real-time computation system
- 14. Storm @ Spotify
- 16. • Workers die -> Cascading JVM Process death • Memcache flakiness • Cassandra JVM problems due to write/overwrite pattern Challenges
- 17. Future/Ongoing Work • Simplify the topology • Keep listens for 24 hours • Ongoing work on other real time personalization features.
- 18. Questions Esh Kumar eshvk@spotify.com Emily Samuels esamuels@spotify.com
Hinweis der Redaktion
- Emily Introduce Emily and Esh give background on what we do and who we are Discover Page Google Now Playlist Recommendations Discover Weekly Personalization features
- Emily
- Emily Scalding
- Esh
- Emily Scalding
- Emily Scalding
- Esh Talk about high intent vs low intent when talking about building user vectors.
- Esh A single machine = 100 billion words a day. Word2vec works on the basis of the distributional hypothesis. The idea being that words which appear in the same context, have similar meanings. One model in the word2vec framework that we use is the Skipgram model. So essentially, what happens is that we go through documents, for each word in the document, we try to predict what the future or previous words should be. Mathematically there is a way to show that this is like factorizing a word-context matrix. For us, playlists are documents, and words are songs that we would like to learn vectors for. The advantage of something like word2vec is that at the end, you have a geometry defined on top of vectors. So you could add the tracks of an artists to get the vector representation of an artist.
- Esh Static indices that can be shipped around. Core principle being LSH.
- Esh reflective of their whole music taste
- Emily We are the first team to build a production ready personalization feature using Storm at Spotify. The Kafka queues were optimized for Hadoop ingestion Localized close to the Hadoop Cluster in London.
- Emily Spouts, bolts, tuples Topology to stitch together the bolts
- Emily First team at Spotify to do real-time recommendations The Kafka queues were optimized for Hadoop ingestion. The Kafka cluster was localized close to the Hadoop cluster. Both in our data center in London. Localized close to the Hadoop Cluster in London.
- Emily Write into LON Cassandra cluster Use sparkey files to store vector info Splash to ship sparkey files not writing user vectors, only writing out the recs
- Esh Despite the challenges, we had a successful ab test and are running this in production
- Emily Write out the vectors, not just the recs Service for vectors Aggregation service on top of vectors to compute recs Use real-time data to improve recs for all users
- Emily Why Lambda The Batch Architecture Real-time Architecture Challenges Future Work