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Recommendation Systems
 

Recommendation Systems

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    Recommendation Systems Recommendation Systems Presentation Transcript

    • Recommender System Mahmut Özge Karakaya
    • Recommender System Helps users find items of interests based on; ● Explicit ratings ● Past transactions ● Item content
    • Recommender System Process; ● Generate model ● Predict items ● Rank
    • History ● Tapestry (1992) ○ Subscribe mail lists ● ACM Recsys (2007 - ...) ● Netflix Prize (2009) ○ 1m$ prize
    • Amazon
    • goodreads
    • Google News
    • eHarmony
    • Every Domain is Unique ● Data ● Time ○ year ○ time of day ○ mood ○ item first appeared ● Item consumed before ● Whose opinion ● Cluster users
    • Solutions ● Apache Mahout ○ hadoop, open source ● Myrrix ○ hadoop, cloud, Cloudera ● easyrec ○ open source, restful ● LensKit ○ open source, movielens
    • Benefits ● Increase on sales ○ Amazon 2006 %35 ● Based on real activity ○ Always Up-To-Date ● Great for discovery ● Right item to right user ● Personalization ● Reduced organizational maintenance ○ Navigation
    • Drawbacks ● Personalized recommenders are difficult to set up ○ Algorithms ○ Scalability ● Maintenance ○ System ○ Monitoring ● Sometimes they’re wrong ● Attacks ○ Outliers
    • Taxonomy of Recommenders ● Content Based Filtering ● Collaborative Filtering ● Hybrid Recommenders
    • Content Based Filtering ● Knowledge Based Filtering ● Learn User Profile
    • Knowledge Based Filtering
    • Learn User Profile ● IMDb ●
    • Collaborative Filtering ● Memory Based ○ Item Based ○ User Based ● Model Based ○ SVD
    • Memory Based ● What is Similarity Matrix? ● Nearest neighbours
    • Memory Based ● Will Eric rent the movie Titanic?
    • Item Based
    • Item Based
    • Item Based ● Similarity ● Prediction
    • User Based
    • User Based ● Similarity ● Prediction
    • SVD ● Prediction
    • SVD ● Learning rule
    • Item Based vs User Based vs SVD ● Least memory: SVD ● Most accurate: SVD ● Explanation: Item Based
    • Content Based vs Collaborative ● Least memory: Content Based ● Least learning: Content Based ● No content needed: Collaborative ● Cold start: Collaborative ● Social: Collaborative ● Shortest Prediction Time: Collaborative
    • Evaluation Setup ● Train Set (~%70) ● Test Set (~%30) ○ Probe Set
    • Taxonomy of Evaluation ● Predicting Ratings ● Recommending Items
    • Predicting Ratings ● Accuracy ○ Mae ○ RMSE
    • Taxonomy of Evaluation ● Recommending Items ○ Accuracy ● Recall ○ Diversity ○ Aggregate Diversity ○ Novelty
    • Recall
    • Diversity
    • Aggregate Diversity ● # of recommended unique items ● Long Tail
    • Novelty where; a is an item, u is number of users. is number of users who rated item a.
    • Fields of Study ● Algorithms ● Evaluation Metrics ● Cross-domain ● Group recommendations ● ...
    • Further Readings ● Recommender Systems Handbook ● https://www.coursera.org/course/recsys ● Mahout in Action ● ACM Conference Papers
    • Recommender System Thank you for listening