Introduction to Recommender Systems
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The document is an introductory tutorial on recommender systems presented at the Strata + Hadoop World in NYC, 2015. It covers the fundamentals of collaborative filtering, content-based recommendations, and hybrid methods, emphasizing their applications in platforms like Netflix and Spotify. Additionally, it discusses practical considerations for feedback, evaluation, and tuning in deploying such systems.
![recs = sim_model.recommend()
>>> nn_model
Class : NearestNeighborsModel
Distance : jaccard
Method : brute force
Number of examples : 195
Number of feature columns : 1
Number of unpacked features : 5170
Total training time (seconds) : 0.0318
talks[‘bow’] = gl.text_analytics.count_words(talks[‘abstract’])
talks[‘tfidf’] = gl.text_analytics.tf_idf(talks[‘bow’])
nn_model = gl.nearest_neighbors.create(talks, ‘id’, features=[‘tfidf’])
nbrs = nn_model.query(talks, label=‘id’, k=50)
sim_model = gl.item_similarity_recommender.create(historical, nearest=nbrs)
>>> historical
+------------+----------+------------------+---------+------------+
| date | time | user | item_id | event_type |
+------------+----------+------------------+---------+------------+
| 2015-02-12 | 07:05:37 | 809c0dc2548cbbc3 | 38825 | like |
| 2015-02-12 | 07:05:39 | 809c0dc2548cbbc3 | 38825 | like |
>>> talks
+------------+------------+-------------------------------+--------------------------------+
| date | start_time | title | tech_tags |
+------------+------------+-------------------------------+--------------------------------+
| 02/20/2015 | 10:40am | The IoT P2P Backbone | [MapReduce, Storm, Docker,... |
| 02/20/2015 | 10:40am | Practical Problems in Dete... | [Storm, Docker, Impala, R,... |
| 02/19/2015 | 1:30pm | From MapReduce to Programm... | [MapReduce, Spark, Apache,... |
| 02/19/2015 | 2:20pm | Drill into Drill: How Prov... | [JAVA, Docker, R, Hadoop, SQL] |
| 02/19/2015 | 4:50pm | Maintaining Low Latency wh... | [Apache, Hadoop, HBase, YA... |
| 02/20/2015 | 4:00pm | Top Ten Pitfalls to Avoid ... | [MapReduce, Hadoop, JAVA, ... |
| 02/20/2015 | 4:00pm | Using Data to Help Farmers... | [MapReduce, Spark, Storm, ... |
| 02/19/2015 | 1:30pm | Sears Hometown and Outlet... | [Hadoop, Spark, Docker, R,... |
| 02/20/2015 | 11:30am | Search Evolved: Unraveling... | [Docker, R, Hadoop, SQL, R... |
| 02/19/2015 | 4:00pm | Data Dexterity: Immediate ... | [Hadoop, NoSQL, Spark, Sto... |
| ... | ... | ... | ... |
+------------+------------+-------------------------------+--------------------------------+
[195 rows x 4 columns]
26](https://image.slidesharecdn.com/strata-nyc-2015-recommendation-systems-150928235216-lva1-app6891/85/Introduction-to-Recommender-Systems-26-320.jpg)
![recs = sim_model.recommend()
>>> si
Class
Schema
------
User I
Item I
Target
Additi
Number
Number
Statis
------
Number
Number
Number
Traini
------
Traini
Settin
>>> nn_model
Class : NearestNeighborsModel
Distance : jaccard
Method : brute force
Number of examples : 195
Number of feature columns : 1
Number of unpacked features : 5170
Total training time (seconds) : 0.0318
talks[‘bow’] = gl.text_analytics.count_words(talks[‘abstract’])
talks[‘tfidf’] = gl.text_analytics.tf_idf(talks[‘bow’])
nn_model = gl.nearest_neighbors.create(talks, ‘id’, features=[‘tfidf’])
nbrs = nn_model.query(talks, label=‘id’, k=50)
sim_model = gl.item_similarity_recommender.create(historical, nearest=nbrs)
27](https://image.slidesharecdn.com/strata-nyc-2015-recommendation-systems-150928235216-lva1-app6891/85/Introduction-to-Recommender-Systems-27-320.jpg)
Introduction to Recommender Systems
- 1. Introduction to Recommender Systems Machine Learning 101 Tutorial Strata + Hadoop World, NYC, 2015 Chris DuBois, Dato
- 2. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 2
- 3. ML for building data products • Products that produce and consume data. • Products that improve as they produce and consume data. • Products that use data to provide a personalized experience. • Personalized experiences increase engagement and retention. 3
- 4. Recommender systems • Personalized experiences through recommendations • Recommend products, social network connections, events, songs, and more • Implicitly and explicitly drive many of experiences you’re familiar with 4
- 5. Recommender uses • Netflix, Spotify, LinkedIn, Facebook with the most visible examples • “You May Also Like” “People You May Know” “People to Follow” • Also silently power many other experiences • Quora/FB/Stitchfix: given interest in A, what else might they be interested in? • Product listings, up-sell options, etc. 5
- 6. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 6
- 7. Basic idea 7 • Data • past behavior • similarity between items • current context • Machine learning models • Input data about users and items • Output a function that provides a list of items for a given context
- 8. recom m end City of God Wild Strawberries The Celebration La Dolce Vita Women on the Verge of a Nervous Breakdown What do I recommend? 8 Collaborative filtering
- 9. City of God Wild Strawberries The Celebration La Dolce Vita Women on the Verge of a Nervous Breakdown 9 Content-based similarity
- 10. What data do you need? • Required for collaborative filtering • User identifier • Product identifier • Required for content-based recommendations • Information about each item • Further customization • Ratings (explicit data), counts • Side data 10
- 11. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 11
- 12. Implicit data • User x product interactions • Consumed / used / clicked / etc. 12
- 14. Item-based CF: predictions 14 Create a ranked list for a given user using the list of previously seen items • For each item, i, compute the average similarity between i and the items in the list • Compute a list of the top N items ranked by score Alternatives • Incorporate rating, e.g., cosine distance • Other distances, e.g., Pearsons
- 15. Demo! 15
- 16. Matrix factorization • Treat users and products as a giant matrix with (very) many missing values • Users have latent factors that describe how much they like various genres • Items have latent factors that describe how much like each genre they are 16
- 17. Matrix factorization • Turn this into a fill-in-the-missing-value exercise by learning the latent factors • Implicit or explicit data • Part of the winning formula for the Netflix Prize • Predict ratings or rankings 17
- 18. 18 Alex Bob Alice Barbara Game of Thrones Vikings House of Cards True Detective Usual Suspects 5 5 5 3 5 4 5 1 5 4 3 5 5 Matrix factorization
- 19. 19 5 5 5 3 5 4 5 1 5 4 3 5 5 Game of Thrones Vikings House of Cards True Detective Usual Suspects Alex Bob Alice Barbara Model parameters Matrix factorization
- 20. 20 5 5 5 3 5 4 5 1 5 4 3 5 5 HBO people Game of Thrones Vikings House of Cards True Detective Usual Suspects Alex Bob Alice Barbara Matrix factorization
- 21. 21 5 5 5 3 5 4 5 1 5 4 3 5 5 HBO people Violent historical Game of Thrones Vikings House of Cards True Detective Usual Suspects Alex Bob Alice Barbara Matrix factorization
- 22. 22 5 5 5 3 5 4 5 1 5 4 3 5 5 HBO people Violent historical Kevin Spacey fans Game of Thrones Vikings House of Cards True Detective Usual Suspects Alex Bob Alice Barbara Matrix factorization
- 23. Fill in the blanks • Learn the latent factors that minimize prediction error on the observed values • Fill in the missing values • Sort the list by predicted rating & recommend the unseen items 23
- 24. Demo! 24
- 25. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 25
- 26. recs = sim_model.recommend() >>> nn_model Class : NearestNeighborsModel Distance : jaccard Method : brute force Number of examples : 195 Number of feature columns : 1 Number of unpacked features : 5170 Total training time (seconds) : 0.0318 talks[‘bow’] = gl.text_analytics.count_words(talks[‘abstract’]) talks[‘tfidf’] = gl.text_analytics.tf_idf(talks[‘bow’]) nn_model = gl.nearest_neighbors.create(talks, ‘id’, features=[‘tfidf’]) nbrs = nn_model.query(talks, label=‘id’, k=50) sim_model = gl.item_similarity_recommender.create(historical, nearest=nbrs) >>> historical +------------+----------+------------------+---------+------------+ | date | time | user | item_id | event_type | +------------+----------+------------------+---------+------------+ | 2015-02-12 | 07:05:37 | 809c0dc2548cbbc3 | 38825 | like | | 2015-02-12 | 07:05:39 | 809c0dc2548cbbc3 | 38825 | like | >>> talks +------------+------------+-------------------------------+--------------------------------+ | date | start_time | title | tech_tags | +------------+------------+-------------------------------+--------------------------------+ | 02/20/2015 | 10:40am | The IoT P2P Backbone | [MapReduce, Storm, Docker,... | | 02/20/2015 | 10:40am | Practical Problems in Dete... | [Storm, Docker, Impala, R,... | | 02/19/2015 | 1:30pm | From MapReduce to Programm... | [MapReduce, Spark, Apache,... | | 02/19/2015 | 2:20pm | Drill into Drill: How Prov... | [JAVA, Docker, R, Hadoop, SQL] | | 02/19/2015 | 4:50pm | Maintaining Low Latency wh... | [Apache, Hadoop, HBase, YA... | | 02/20/2015 | 4:00pm | Top Ten Pitfalls to Avoid ... | [MapReduce, Hadoop, JAVA, ... | | 02/20/2015 | 4:00pm | Using Data to Help Farmers... | [MapReduce, Spark, Storm, ... | | 02/19/2015 | 1:30pm | Sears Hometown and Outlet... | [Hadoop, Spark, Docker, R,... | | 02/20/2015 | 11:30am | Search Evolved: Unraveling... | [Docker, R, Hadoop, SQL, R... | | 02/19/2015 | 4:00pm | Data Dexterity: Immediate ... | [Hadoop, NoSQL, Spark, Sto... | | ... | ... | ... | ... | +------------+------------+-------------------------------+--------------------------------+ [195 rows x 4 columns] 26
- 27. recs = sim_model.recommend() >>> si Class Schema ------ User I Item I Target Additi Number Number Statis ------ Number Number Number Traini ------ Traini Settin >>> nn_model Class : NearestNeighborsModel Distance : jaccard Method : brute force Number of examples : 195 Number of feature columns : 1 Number of unpacked features : 5170 Total training time (seconds) : 0.0318 talks[‘bow’] = gl.text_analytics.count_words(talks[‘abstract’]) talks[‘tfidf’] = gl.text_analytics.tf_idf(talks[‘bow’]) nn_model = gl.nearest_neighbors.create(talks, ‘id’, features=[‘tfidf’]) nbrs = nn_model.query(talks, label=‘id’, k=50) sim_model = gl.item_similarity_recommender.create(historical, nearest=nbrs) 27
- 28. Side features • Include information about users • Geographic, demographic, time of day, etc. • Include information about products • Product subtypes, geographic availability, etc. 28
- 29. Demo! 29
- 30. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 30
- 34. Current approaches Downsides Alternatives Linear model + Matrix factorization Factorization machines with side data Ensembles Black box Hard to tune Hard to explain Composite distance + nearest neighbors Directly tune the notion of distance Easy to explain Hybrid methods 34 Benefits Cold start situations Incorporating context
- 36. Features Composite distances Distance Weight year Euclidean 1.0 description Jaccard 0.5 genre Jaccard 1.5 latent factors cosine 1.5
- 37. Features Composite distances Distance Weight year Euclidean 1.0 description Jaccard 0.5 genre Jaccard 1.5 latent factors cosine 1.5
- 38. Features Composite distances Distance Weight year Euclidean 1.0 description Jaccard 0.5 genre Jaccard 1.5 latent factors cosine 1.5
- 39. Features Composite distances Distance Weight year Euclidean 1.0 description Jaccard 0.5 genre Jaccard 1.5 latent factors cosine 1.5
- 40. Demo! 40
- 41. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 41
- 42. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 42
- 43. Feedback Core assumption past behavior will help predict future behavior. Collaborative filtering data often comes from log data. Plan ahead! • value elicitation, e.g., like, watch, etc. • ratings, stars, etc. • critique, e.g. Improve the system’s recommendations! • preference: e.g., Which do you prefer? Preprocessing • Item deduplication Relationship to information retrieval • position bias • source of the event 43
- 44. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 44
- 45. Evaluating Models 45 Historical Data Live Data PredictionsTrained Model Deployed Model Offline Evaluation Online Evaluation
- 46. Evaluation • Train on a portion of your data • Test on a held-out portion • Ratings: RMSE • Ranking: Precision, recall • Business metrics • Evaluate against popularity 46
- 47. Rankings? • Often less concerned with predicting precise scores • Just want to get the first few items right • Screen real estate is precious • Ranking factorization recommender 47
- 48. Evaluation: Example Suppose we serve a ranked list of 20 recommendations. “relevant” == user actual likes an item “retrieved” == set of recommendations Precision@5 % of top-5 recommendations that user likes Precision@20 % of recommendations that user likes Questions What if only 5 are visible? How do things vary based on the number of events? 48
- 49. Demo! 49
- 50. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 50
- 51. Model parameter search • Searching for which model performs best at your metric • Strategies • grid search • random search • Bayesian optimization 51
- 52. How to choose which model? • Select the appropriate model for your data (implicit/explicit), if you want side features or not, select hyperparameters, tune them… • … or let GraphLab Create do it for you and automatically tune hyperparameters 52
- 53. Outline Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 53
- 56. Predictive Service User activity logged Request for Strata event data Personalized recommendations 56
- 57. Summary Motivation Fundamentals Collaborative filtering Content-based recommendations Hybrid methods Practical considerations Feedback Evaluation Tuning Deployment 57
- 58. Thank you! 58 Email Twitter chris@dato.com @chrisdubois