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Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013

Reco4J Project Intelligent RecommendaDons for

Your Business

Alessandro Negro – Luigi Giuri Reco4J Project @ Paris Meetup -‐ May 2013 Page 1

Recommender Systems •  A system that can recommend or present items to the user based on the user’s interests and interacDons

•  One of the best ways to provide a personalized customer experience

•  Built by exploiDng collecDve intelligence to perform predicDons

•  Examples: Amazon, YouTube, NeUlix, Yahoo, Tripadvisor, Last.fm, IMDb


The Example: NeUlix •  The world largest online movie rental services, 33 million members in 40 countries

•  60% of members selecDng movies based on recommendaDons (September 2008)

•  NeUlix Prize: US$ 1,000,000 was given to the BellKor's PragmaDc Chaos team which bested NeUlix's own algorithm for predicDng raDngs by 10.06% (September 2009)

•  75% of the content watched on the service comes from its recommendaDon engine (April 2012)


Why Recommender Systems •  Standard uses:

–  Increase the number of items sold –  Sell more diverse items –  Increase the user saDsfacDon –  Increase user fidelity –  Beger understand what the user wants

•  Advanced uses: –  Create ad hoc campaigns (per geographic area, per type of users) –  OpDmize products distribuDon over a wide area for large retail chains


Problem •  There are no available sohware products for state-‐of-‐the-‐art recommender systems

•  A high-‐end recommender engine can be built only through expensive custom projects

•  Large scale user/item datasets require a big data approach

•  There is no "best soluDon" •  There is no "one soluDon fits all” •  The NeUlix winner composed 104 different algorithms


SoluDon: Reco4J

A graph-‐based recommender engine


Reco4J Main Goals •  Implement the state-‐of-‐the-‐art in the recommendaDon on top of a graph model

•  Provide sohware / cloud services / consultancy

•  Contribute to the RecSys research field


Reco4J Features •  Composable models/algorithms •  Persistent models (mulD model supported) •  Updatable models •  Commercial and research-‐oriented algorithms •  Independent from source knowledge datasets •  Cluster and cloud-‐ready •  Context-‐aware recommendaDons •  MulDtenant •  Social recommendaDons


Reco4J Under the Hood •  J is for Java •  CollaboraDve filtering algorithms –  Neighborhood-‐based methods –  Latent factor models

•  Neo4J graph database: –  Data source repository –  Persistent model repository

•  Hadoop cluster/MapReduce •  Apache Mahout


Algorithms Roadmap •  CollaboraDve filtering

–  Memory based (Neighborhood) •  User/Item based

–  Several distance algorithms (Cosine, Euclidean, Tanimoto, etc.) •  Graph based

–  Path Based Similarity (Shortest Path, Number of Paths) –  Random Walk Similarity (Item Rank, Average first-‐passage/commute Dme)

–  Model based (Latent factor) •  StochasDc gradient descendant •  AlternaDng least square

•  Social recommendaDon –  Trust based approach –  ProbabilisDc approach


Algorithms Roadmap (2) •  Crosswise Features – Context awareness – Composability – Real-‐Dme – ParallelizaDon


Context-‐Aware RecommendaDon “The ability to reach out and touch customers anywhere means that companies must deliver not just compe;;ve products but also unique, real-‐;me customer experiences shaped by customer context”

C. K. Prahalad

•  Incorporate contextual informa5on in the recommendaDon process •  Modeling contextual InformaDon

–  From: User x Item -‐> RaDng –  To: User x Item x Context -‐> RaDng

•  Hierarchical structure •  Three approaches

–  Contextual pre-‐filtering –  Contextual post-‐filtering –  Contextual modeling


Reco4J Context Model

users items rate(c1, …, cn)

………….

contextualized ra;ngs

Context Tuple DAG

usera-‐mp1 itemc-‐split1

…….. usera-‐mp2

itemc-‐split2

itemd-‐splitk

userb-‐mpk

…….. ……..

user micro profiles item spliEng


Advantage of graph database •  NoSQL database to handle BigData issue •  Extensibility •  No aggregate-‐oriented database •  Minimal informaDon needed •  Natural way for represenDng connecDons:

–  User -‐ to -‐ item –  Item -‐ to -‐ item –  User -‐ to -‐ User

•  Graph Based/Social Algorithms •  Graph ParDDoning (sharding) •  Performance


Example: Find Neighbors


Why Neo4J? •  Java based •  Embeddable/Extensible •  NaDve graph storage with naDve graph processing engine

•  Open Source, with commercial version •  Property Graph •  ACID support •  Scalability/HA •  Comprehensive query/traversal opDons


RecommendaDon Model


Persistence Model


Reco4J + Hadoop •  Queue Based Process •  Operates both on cluster and cloud •  Each process downloads data from

Neo4J/Reco4J before or during computaDon

•  Stores data into Reco4J Model

•  Scaling augmenDng the number of: •  Neo4J Nodes (only one master) •  Hadoop Nodes


Reco4J in the Cloud •  Recommenda)on as a service (RaaS) •  Reco4J cloud infrastructure offers: –  Pay as you need –  Pay as you grow –  Support for burst –  Periodical analysis at lower costs –  Test/evaluate several algorithms on a reduced dataset –  Compose algorithms dynamically


Consultancy Goals

Analysis

Data Source

ExploraDon

Process DefiniDon

Import Data

Test/EvaluaDon

Deploy


Thank you

Alessandro Negro Linkedin: hgp://it.linkedin.com/in/alessandronegro/ Email: [email protected] Luigi Giuri Linkedin: it.linkedin.com/in/luigigiuri Email: [email protected] Reco4J Site: hgp://www.reco4j.org Twiger: @reco4j GitHub: hgps://github.com/reco4j

Download - Reco4 @ Paris Meetup (May 20th)

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