Recommendations in Drupal (Drupal DevDays Barcelona 2012)

transcript

Drupal Developer Days Barcelona 2012.06.16

Personalisation and Recommendations using Drupal

• Keywords:– Personalisation– Recommendations– Scalable machine learning– Predictions– Similarity– Data Mining– Big Data– Trend Spotting– Clustering

Kendra Initiative

• Mission– Foster an Open Distributed Marketplace for Digital

Media• EU funded– P2P-Next• http://www.p2p-next.org

– SARACEN = Socially Aware, collaboRative, scAlable Coding mEdia distributioN• http://www.saracen-p2p.eu

Deliverables

• Kendra Signpost– Metadata interoperability, mapping and transformation

• Smart Filters– Portable preferences and filters

• Kendra Social, Kendra Hub– Social networking management tools

• Standards work– OpenSocial extension– Social API – see Abstracting Social Networking functionality in

Drupal sprint• Kendra Match

– Searching and recommendation

Components

• Drupal Recommender API module• Recommender helper modules• async_command module• Apache Mahout or cloud service• Hadoop cluster (optional)

Industry Examples

• Amazon• Netflix• Spotify, Pandora• Facebook, LinkedIn• OKCupid• iTunes: Genius; app store - not so much

Machine learning

• Collaborative Filtering– AKA recommender engines

• Clustering• Classification

Collaborative Filtering

• Input: preference data• Output: predictions• Preference = <uid1, (nid1 or uid2), w1>– w1 = signed integer representing weight of uid1-

nid1 or uid1-uid2 correlation (affinity)

• Prediction = <uid1, (nid1or uid2), w2>– w2 = float representing strength of uid1-nid1 or

uid1-uid2 correlation

Enter Mahout

• Apache Mahout is a scalable machine learning library that supports large data sets.

• Launched Spring 2010• Grew from the Apache Lucene project (basis

for Apache Solr)• Merged with Taste project

Use Cases

• Recommendation mining• Clustering• Classification• Frequent itemset mining

Out-of-box algorithms• Recommendation

– User-based recommender– Item-based recommender– Slope-One recommender– Distributed Item-Based Collaborative Filtering– Collaborative Filtering using parallel matrix factorisation

• Clustering– Canopy Clustering– K-Means Clustering– Fuzzy K-Means– Mean Shift Clustering– Dirichlet Process Clustering– Latent Dirichlet Allocation– Spectral Clustering– Minhash Clustering

• Model combination– Naive Bayes algorithm

Hadoop

• Provides clustering capabilities• Not trivial to set up• Not yet implemented in Recommender API

(issue #1206840)

Recommender API

• Drupal 7 (alpha) & 6 (beta)• Can run either on same server as Apache web

server or on a remote server• Java helper program (was PHP)• Uses JDBC and Java Persistence API (JPA) • Drupal helper modules

Recommender API helper modules

• Browsing History Recommender• OG Similar groups module• Ubercart Products Recommender• Fivestar Recommender• Points Voting Recommender• Flag Recommender

Asynchronous operation

• Async_command module– Talks to Mahout– Typically run via cron

• Results are stored directly in Drupal db– Recommender tables– Via JDBC

Hosting Solutions

• Self-hosted: all-in-one (web server, database server, recommender server) - has its pro’s & cons

• Recommender API Cloud Service - looking for beta testers

• Amazon Elastic MapReduce (EMR)

Installing Mahout

• Prerequisites:– Dedicated VM if possible– Linux, Mac OSX Leopard 10.5.6 or later, Windows

(Cygwin)– Java JDK 1.6– Maven 2.0.11 or higher (maven.apache.org)

Installing Mahout

• Building– Follow instructions– https://cwiki.apache.org/MAHOUT/

buildingmahout.html• Use maven to build examples

Installing Mahout

• Testing: Grouplens– On a single 2GHz server:• 100K ratings (1000 users, 1700 items): 9 minutes. 1M

ratings (6000 users, 4000 items): 12 hours. 10M ratings (72,000 users, 10,000 items): fuggedaboutit

– Using 6 concurrent 2GHz processing units:• 100K ratings (1000 users, 1700 items): 2 minutes. 1M

ratings (6000 users, 4000 items): 2 hours. 10M ratings (72,000 users, 10,000 items): 11 days 20 hours.

Installing Recommender API

• See http://drupal.org/node/1207634• Configuration– sites/all/modules/async_command/

config.properties should match settings.php• Download and enable async_command• Check

/admin/config/search/recommender/admin

• Making recommendations– User-user– User-item– Item-item

• Predictions/similarity feeds back into Drupal• Blocks• Views

Case study: Data Mining and Recommendations in SARACEN

• SARACEN: http://www.saracen-p2p.eu/• Feedback loop to measure subjective quality of the

recommendations– Limited set of data, small user base– API provides an initial set of recommended videos– User can then watch a recommended video– User’s actions are incorporated into their implicit

profile, feeds back to the recommender API– Recommender API generates new predictions based on

the complete set of implicit profile metadata

SARACEN: Prototype

Recommender data sources

• Explicit data– SARACEN account data, including location and language– Linked accounts and profiles

• e.g. Facebook user profile, “likes”, connections, metadata

• Implicit data– Activity history recorded during the user’s sessions– Searches– Shared content– Viewed content– Albums (media containers)– Content ratings

Scalability

• Don’t need Hadoop if– Number of users is orders of magnitude larger

than the number of items– Users browse anonymously most of the time– Few users log in and need personalised

recommendations– Item churn rate is relatively low

Worth Considering

• Decreased Transparency• Decreased Serendipity• Sleep deprivation

Resources: Recommender API

• http://drupal.org/project/recommender• http://recommenderapi.com/cloud• https://cwiki.apache.org/confluence/display/

MAHOUT

Resources: Mahout

• http://mahout.apache.org/• Mahout in Action– http://www.manning.com/owen/– ISBN 9781935182689.

• The Optimality of Naive Bayes, Harry Zhang.• http://aws.amazon.com/elasticmapreduce/

Acknowledgements

• Socially Aware, collaboRative, scAlable Coding mEdia distributioN (SARACEN)– http://www.saracen-p2p.eu– Funded within the European Union’s Seventh

Framework Programme (FP7/2007-2013) under grant agreement 248474

Questions/comments…

• Kendra Initiative– @kendra– http://www.kendra.org.uk– https://github.com/kendrainitiative

• Klokie Grossfeld– @klokie– klokie@kendra.org.uk– http://www.linkedin.com/in/klokie

• Daniel Harris– @dahacouk– daniel@kendra.org.uk

Recommendations in Drupal (Drupal DevDays Barcelona 2012)

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