1. Recommendation Systems ARGEDOR

2. Introduction Sample Data Tools Cases

3. Introduction Recommender systems reduce information overload by estimating relevance. Which artist should I listen based on my preferences? What is the best holiday for me and my family? Which movie should I watch? Which web sites will I find interesting? Which book should I buy for my next vacation?

4. Personalized Recommendation

5. Collaborative Filtering Collaborative: "Tell me what's popular among my peers"

6. Content Based Recommendation Content-based: "Show me more of the same what I've liked"

7. Knowledge Based Recommendation Knowledge-based: "Tell me what fits based on my needs"

8. Hybrid Models Hybrid: combinations of various inputs and/or composition of different mechanism

9. Sample Data: MovieLens 1M dataset Content Data Item movies.dat MovieID::Title::Genres 1::Toy Story (1995)::Animation|Children's|Comedy Sometimes content data contains time of creation for the content. (http://www.grouplens.org/datasets/movielens/ )

10. Sample Data: MovieLens 1M dataset Content Data User users.dat UserID::Gender::Age::Occupation::Zip-code 1::F::1::10::48067 !! Since this data anonymized no user name related information.

11. Sample Data:TTNet Music TTNet Music User Rating Logs userId,songId,albumId,artistId,timeofaction,ratingValue,channel 2330295,3313069,286068,546697,2013-03-26 15:17:49,0.9,SI – Rating value is a derived value obtained by a formula depending on user’s actions(listened,downloaded,listened before etc) – For TTNET music recommendation engine we have approximately 1 million unique user action logs daily. – Stored on distributed file system. Used for collaborative filtering.

12. User Profiling Content data Age: 18 Gender: F Occupation: 45674 User’s Ratings Item1:3 Item2:5 User profiling enables weighting of similarity metrics

13. Context Awareness Context location time of day season mood weather Context information is taken into account when generating recommendations

14. Tools Apache Mahout (http://mahout.apache.org/)http://mahout.apache.org/ Open Source machine learning library for large scale applications – Classification( Complementary Naive Bayes classifier, Random forest decision tree based classifier ) – Clustering( K-Means, Fuzzy K-Means clustering ) – Collaborative filtering,User based,Item based recommendations.

15. Tools hadoop.apache.org Open source distributed file system. – Large Scale DBMS runs on Hadoop file system.

16. Tools http://www.neo4j.org/ Open source graph database. – Storage for highly connected data – Fast query response for large scale databases – Most graph traversal algoritms implemented. – Instead of scaning whole database just visit connected parts. – Collaborative filtering data model is possible with Neo4j. – Used for Content based music recommendation projects of ARGEDOR

17. Example:Movie Graph DB Relations Both content data and user actions are stored on graph db https://github.com/neo4j-examples/cineasts