Distributed Itembased Collaborative Filtering with Apache Mahout. Sebastian Schelter twitter.com/sscdotopen. 7.
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1 Distributed Itembased Collaborative Filtering with Apache Mahout Sebastian Schelter twitter.com/sscdotopen 7. October 2010
2 Overview 1. What is Apache Mahout? 2. Introduction to Collaborative Filtering 3. Itembased Collaborative Filtering 4. Computing similar items with Map/Reduce 5. Implementations in Mahout 6. Further information Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 2
3 What is Apache Mahout? A scalable Machine Learning library scalable to reasonably large datasets (core algorithms implemented in Map/Reduce, runnable on Hadoop) scalable to support your business case (Apache License) scalable community Usecases Clustering (group items that are topically related) Classification (learn to assign categories to documents) Frequent Itemset Mining (find items that appear together) Recommendation Mining (find items a user might like) Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 3
4 Recommendation Mining = Help users find items they might like Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 4
5 Users, Items, Preferences Terminology users interact with items (books, videos, news, other users,...) preferences of each user towards a small subset of the items known (numeric or boolean) Algorithmic problems Prediction: Estimate the preference of a user towards an item he/she does not know Use Prediction for Top-N-recommendation: Find the N items a user might like best Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 5
6 Explicit and Implicit Ratings Where do the preferences come from? Explicit Ratings users explictly express their preferences (e.g. ratings with stars) willingness of the users required Implicit Ratings interactions with items are interpreted as expressions of preference (e.g. purchasing a book, reading a news article) interactions must be detectable Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 6
7 Collaborative Filtering How does it work? the past predicts the future: all predictions are derived from historical data (the preferences you already know) completely content agnostic very popular (e.g. used by Amazon, Google News) Mathematically user-item-matrix is created from the preference data task is to predict missing entries by finding patterns in the known entries Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 7
8 A sample user-item-matrix The Matrix Alien Inception Alice Bob? 2 5 Peter Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 8
9 Itembased Collaborative Filtering Algorithm neighbourhood-based approach works by finding similarly rated items in the user-item-matrix estimates a user's preference towards an item by looking at his/her preferences towards similar items Highly scalable item similarities tend to be relatively static, can be precomputed offline periodically less items than users in most scenarios looking at a small number of similar items is sufficient Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 9
10 Example Similarity of The Matrix and Inception rating vector of The Matrix : (5,-,4) rating vector of Inception : (4,5,2) isolate all cooccurred ratings (all cases where a user rated both items) pick a similarity measure to compute a similarity value between -1 and 1 e.g. Pearson-Correlation corr i, j = u U R u, i R i R u, j R j u U R u,i R i u U R u, j R j =0.47 Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 10
11 Example Prediction: Estimate Bob's preference towards The Matrix look at all items that a) are similar to The Matrix b) have been rated by Bob => Alien, Inception estimate the unknown preference with a weighted sum P Bob, Matrix = s Matrix, Alien r Bob, Alien s Matrix, Inception r Bob, Inception s Matrix, Alien s Matrix, Inception =1.5 Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 11
12 Algorithm in Map/Reduce How can we compute the similarities efficiently with Map/Reduce? Key ideas we can ignore pairs of items without a cooccurring rating we need to see all cooccurring ratings for each pair of items in the end Inspired by an algorithm designed to compute the pairwise similarity of text documents Mahout's implementation is more generalized to be usable with other similarity measures, see DistributedVectorSimilarity and RowSimilarityJob for more details Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 12
13 Algorithm in Map/Reduce - Pass 1 Map - make user the key (Alice,Matrix,5) (Alice,Alien,1) (Alice,Inception,4) (Bob,Alien,2) (Bob,Inception,5) (Peter,Matrix,4) (Peter,Alien,3) (Peter,Inception,2) Alice (Matrix,5) Alice (Alien,1) Alice (Inception,4) Bob (Alien,2) Bob (Inception,2) Peter (Matrix,4) Peter (Alien,3) Peter (Inception,2) Reduce - create inverted index Alice (Matrix,5) Alice (Alien,1) Alice (Inception,4) Bob (Alien,2) Bob (Inception,5) Peter (Matrix,4) Peter (Alien,3) Peter (Inception,2) Alice (Matrix,5)(Alien,1)(Inception,4) Bob (Alien,2)(Inception,5) Peter (Matrix,4)(Alien,3)(Inception,2) Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 13
14 Algorithm in Map/Reduce - Pass 2 Map - emit all cooccurred ratings Alice (Matrix,5)(Alien,1) (Inception,4) Matrix,Alien (5,1) Matrix,Inception (5,4) Alien,Inception (1,4) Bob (Alien,2)(Inception,5) Alien,Inception (2,5) Peter (Matrix,4)(Alien,3) (Inception,2) Matrix,Alien (4,3) Matrix,Inception (4,2) Alien,Inception(3,2) Reduce - compute similarities Matrix,Alien (5,1) Matrix,Alien (4,3) Matrix,Inception (5,4) Matrix,Inception (4,2) Alien,Inception (1,4) Alien,Inception (2,5) Alien,Inception (3,2) Matrix,Alien ( 0.47) Matrix,Inception (0.47) Alien,Inception ( 0.63) Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 14
15 Implementations in Mahout ItemSimilarityJob computes all item similarities various configuration options: similarity measure to use (e.g. cosine, Pearson-Correlation, Tanimoto-Coefficient, your own implementation) maximum number of similar items per item maximum number of cooccurrences considered... Input: preference data as CSV file, each line represents a single preference in the form userid,itemid,value Output: pairs of itemids with their associated similarity value Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 15
16 Implementations in Mahout RecommenderJob Distributed Itembased Recommender various configuration options: similarity measure to use number of recommendations per user filter out some users or items... Input: the preference data as CSV file, each line contains a preference in the form userid,itemid,value Output: userids with associated recommended itemids and their scores Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 16
17 Further information Mahout's website, wiki and mailinglist Mahout in Action, available through Manning's Early Access Program B. Sarwar et al: Itembased collaborative filtering recommendation algorithms, 2001 T. Elsayed et al: Pairwise document similarity in large collections with MapReduce, 2008 Sebastian Schelter: Distributed Itembased Collaborative Filtering with Apache Mahout 17
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