What are product recommendations, and how do they work?
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The content of this presentation is based on: Chapter 1, 2 and 4 of the following book: Owen, Anil, Dunning, Friedman. Mahout in Action. Shelter Island, NY: Manning Publications Co., 2012. Chapter Discussion of Similarity Metrics of the following publication: Shanley Philip. Data Mining Portfolio.
![Septem
ber 1,
2012
The output could be: [item:104, value:4.257081]" 16
The recommender engine did so because it
estimated user 1s preference for 104 to be
about 4.3, and that was the highest among all
the items eligible for recommendation.
Questions:
≒ Is this the best recommendation for user 1?
≒ What exactly is a good recommendation?
A Simple user-based Example
Analyzing the Output](https://image.slidesharecdn.com/nam-ecommerce-collaborativfiltering-120901065505-phpapp02/85/What-are-product-recommendations-and-how-do-they-work-16-320.jpg)
![Septem
ber 1,
2012
A Simple Recommender 22
class RecommenderExample {"
main(String[] args) throws {"
DataModel model = new FileDataModel(new File(examle.csv")); "
UserSimilarity similarity = "
new PearsonCorrelationSimilarity(model);"
UserNeighborhood neighborhood = "
new NearestNUserNeighborhood(2, similarity, model);"
Recommender recommender = "
new GenericUserBasedRecommender(model, neighborhood, similarity);"
List<RecommendedItem> recommendations = recommender.recommend(1, 1);"
" for (RecommendedItem recommendation : recommendations) {"
System.out.println(recommendation);"
}"
}}"
A Simple user-based Example
Create a Recommender](https://image.slidesharecdn.com/nam-ecommerce-collaborativfiltering-120901065505-phpapp02/85/What-are-product-recommendations-and-how-do-they-work-22-320.jpg)
What are product recommendations, and how do they work?
- 1. CC 2.0 by Horia Varlan | http://鍖ic.kr/p/7vjmof
- 2. Septem ber 1, 2012 ≒ What are Product Recommenders 2 ≒ Introducing Recommenders ≒ A Simple Example ≒ Recommender Evaluation ≒ How do they work? ≒ Machine learning tool Apache Mahout Namics Conference 2012 Agenda
- 3. Septem ber 1, 2012 ≒ Spin-o鍖 of MeMo News AG, the 3 leading provider for Social Media Monitoring & Analytics in Switzerland ≒ Big Data expert, focused on Hadoop, HBase and Solr ≒ Objective: Transforming data into insights Intro About Sentric
- 4. CC 2.0 by Dennis Wong | http://鍖ic.kr/p/6C3RuV
- 5. Septem ber 1, 2012 ≒ Each day we form opinions about 5 things we like, dont like, and dont even care about. ≒ People tend to like things ≒ that similar people like ≒ that are similar to other things they like ≒ These patterns can be used to predict such likes and dislikes. Introducing Recommenders The Patterns
- 6. Septem ber 1, 2012 user-based Look to what people with 6 similar tastes seem to like Example: Introducing Recommenders Strategies for Discovering New Things
- 7. Septem ber 1, 2012 item-based Figure out what items are 7 like the ones you already like (again by looking to others apparent preferences) Example: Introducing Recommenders Strategies for Discovering New Things
- 8. Septem ber 1, 2012 content-based Suggest items based on 8 Septem particular attribute (again by looking to others apparent ber 1, 2012 preferences) Example: Introducing Recommenders Strategies for Discovering New Things
- 9. Septem ber 1, 2012 9 Collaborative Filtering Item-based Producing recommendations based on, and only based on, knowledge of users User-based Content-based relationships to items. Recommenders Recommendation is all about predicting patterns of taste, and using them to discover new and desirable things you didnt already know about. Introducing Recommenders The De鍖nition of Recommendation
- 10. CC 2.0 by Will Scullin | http://鍖ic.kr/p/6K9jb8
- 11. Septem ber 1, 2012 ≒ Lets start with a simple example 11 Create Input Create a Analyse the Data Recommender Output A Simple user-based Example The Work鍖ow
- 12. Septem ber 1, 2012 ≒ Recommendations will 1,101,5.0 12 1,102,3.0 base on input-data User 1 has a preference 3.0 1,103,2.5 for item 102 2,101,2.0 ≒ Data takes the form of 2,102,2.5 preferences associations 2,103,5.0 2,104,2.0 from users to items 3,101,2.5 3,104,4.0 3,105,4.5 3,107,5.0 Example: 4,101,5.0 4,103,3.0" 4,104,4.5" These values might be ratings 4,106,4.0" on a scale of 1 to 5, where 1 5,101,4.0" 5,102,3.0" indicates items the user cant 5,103,2.0" 5,104,4.0" stand, and 5 indicates 5,105,3.5" favorites. 5,106,4.0 " A Simple user-based Example Input Data
- 13. Septem ber 1, 2012 ≒ Trend visualization for positive users 1,101,5.0 13 1,102,3.0 preferences (in petrol) 1,103,2.5 2,101,2.0 2,102,2.5 1 5 3 2,103,5.0 2,104,2.0 3,101,2.5 3,104,4.0 3,105,4.5 101 102 103 104 105 106 107 3,107,5.0 4,101,5.0 4,103,3.0" 4,104,4.5" 4,106,4.0" 5,101,4.0" 2 4 5,102,3.0" 5,103,2.0" 5,104,4.0" ≒ All other preferences are recognized as 5,105,3.5" negative the user doesnt seem to like the 5,106,4.0 " item that much (red, dotted) A Simple user-based Example Trend Visualization
- 14. Septem ber 1, 2012 Users 1 and 5 seem to have similar tastes. 14 Both like 101, like 102 a little less, and like 103 less still 1 5 101 102 103 104 105 106 107 Users 1 and 4 seem to have similar tastes. Both 2 4 seem to like 101 and 103 identically Users 1 and 2 have tastes that seem to run counter to each other A Simple user-based Example Trend Visualization
- 15. Septem ber 1, 2012 So what product might be recommended to 15 user 1? 1 5 3 101 102 103 104 105 106 107 2 4 Obviously not 101, 102 or 103. User 1 already knows about these. A Simple user-based Example Analyzing the Output
- 16. Septem ber 1, 2012 The output could be: [item:104, value:4.257081]" 16 The recommender engine did so because it estimated user 1s preference for 104 to be about 4.3, and that was the highest among all the items eligible for recommendation. Questions: ≒ Is this the best recommendation for user 1? ≒ What exactly is a good recommendation? A Simple user-based Example Analyzing the Output
- 17. CC 2.0 by larsaaboe | http://鍖ic.kr/p/7nJpV8
- 18. Septem ber 1, 2012 Goal: 18 Evaluate how closely the estimated preferences match the actual preferences. How? Produce Compare estimate estimates with Reasonable 30% for test Prepare Split Run preferences Analyse test data data set 70 % for training with training Calculate a data score Experiment with other recommenders A Simple user-based Example Evaluating a Recommender
- 19. Septem ber 1, 2012 Example evaluation output for a 19 particular recommender engine Item 1 Item 2 Item 3 Actual 3.0 5.0 4.0 Estimate 3.5 2.0 5.0 Di鍖erence 0.5 3.0 1.0 Average distance = (0.5+3.0+1.0)/3=1.5 Root-mean-square =((0.52+3.02+1.02)/3)=1.8484 Note: A score of 0.0 would mean perfect estimation A Simple user-based Example Evaluating a Recommender
- 20. CC 2.0 by amtrak_russ | http://鍖ic.kr/p/6fAPej
- 21. Septem ber 1, 2012 ≒ Mahout 21 ≒ Open-source machine learning library from Apache (Java) ≒ Can be used for large data collections its scalable, build upon Apache Hadoop ≒ Implements algorithms such as Classi鍖cation, Recommenders, Clustering ≒ Incubates a number of techniques and algorithms ≒ ML its a hype! But In a Nutshell Apache Mahout
- 22. Septem ber 1, 2012 A Simple Recommender 22 class RecommenderExample {" main(String[] args) throws {" DataModel model = new FileDataModel(new File(examle.csv")); " UserSimilarity similarity = " new PearsonCorrelationSimilarity(model);" UserNeighborhood neighborhood = " new NearestNUserNeighborhood(2, similarity, model);" Recommender recommender = " new GenericUserBasedRecommender(model, neighborhood, similarity);" List<RecommendedItem> recommendations = recommender.recommend(1, 1);" " for (RecommendedItem recommendation : recommendations) {" System.out.println(recommendation);" }" }}" A Simple user-based Example Create a Recommender
- 23. Septem ber 1, 2012 23 <<interface>> UserSimilarity <<interface>> <<interface>> ApplicaAon Recommender DataModel <<interface>> UserNeighborhood A user-based Recommender Component Interaction
- 24. Septem ber 1, 2012 NearestNUserNeighborhood ThresholdUserNeighborhood 24 2 2 1 1 5 5 3 3 4 4 A neighborhood around user 1 is chosen to consist of the De鍖ning a neighborhood of three most similar users: 5, 4, most-similar users with a and 2 similarity threshold Algorithms UserNeighborhood
- 25. Septem ber 1, 2012 Implementations of this interface de鍖ne a 25 notion of similarity between two users. Implementations should return values in the range -1.0 to 1.0, with 1.0 representing perfect similarity. <<interface>> UserSimilarity" EuclideanDistance PearsonCorrelation UncenteredCosine Similarity" Similarity" Similarity" LogLikelihood TanimotoCoefficient ..." Similarity" Similarity" Algorithms User Similarity
- 26. Septem ber 1, 2012 Similarity between data objects can be represented in 26 a variety of ways: ≒ Distance between data objects is sum of the distances of each attribute of the data objects (i.e. Euclidean Distance) ≒ Measuring how the attributes of both data objects change with respect to the variation of the mean value for the attributes (Pearson Correlation coe鍖cient) ≒ Using the word frequencies for each document, the normalized dot product of the frequencies can be used as a measure of similarity (cosine similarity) ≒ An a few more .. Algorithms User Similarity
- 27. Septem ber 1, 2012 Similarity between 27 two data objects: 5 4 User 5 User 1 3 102 User 2 2 1 User 3 User 4 0 0 1 2 3 4 5 101 Mathematically & Plot Euclidean Distance
- 28. Septem ber 1, 2012 Similarity between 28 two data objects: 5 4.5 4 104 101 3.5 3 102 User 5 2.5 2 103 1.5 1 0.5 0 0 1 2 3 4 5 User 1 Mathematically & Plot Pearson Correlation
- 29. Septem ber 1, 2012 29 Questions? Jean-Pierre K旦nig, jean-pierre.koenig@sentric.ch Namics Conference 2012 Thank you!
- 30. Septem ber 1, 2012 ≒ References 30 The content of this presentation is based on: ≒ Chapter 1, 2 and 4 of the following book: Owen, Anil, Dunning, Friedman. Mahout in Action. Shelter Island, NY: Manning Publications Co., 2012. ≒ Chapter Discussion of Similarity Metrics of the following publication: Shanley Philip. Data Mining Portfolio. ≒ Links http://bitly.com/bundles/jpkoenig/1 A Simple user-based Example Literatur & Links