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Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
  
Reco4J	
  Project	
  
Intelligent	
  RecommendaAons	
  for	
  
Your	
  Business	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  1	
  
Recommender	
  Systems	
  
•  A	
  system	
  that	
  can	
  recommend	
  or	
  present	
  items	
  
to	
  the	
  user	
  based	
  on	
  the	
  user’s	
  interests	
  and	
  
interacAons	
  
•  One	
  of	
  the	
  best	
  ways	
  to	
  provide	
  a	
  personalized	
  
customer	
  experience	
  
•  Built	
  by	
  exploiAng	
  collecAve	
  intelligence	
  to	
  
perform	
  predicAons	
  
•  Examples:	
  Amazon,	
  YouTube,	
  NeSlix,	
  Yahoo,	
  
Tripadvisor,	
  Last.fm,	
  IMDb	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  2	
  
The	
  Example:	
  NeSlix	
  
•  The	
  world	
  largest	
  online	
  movie	
  rental	
  services,	
  33	
  
million	
  members	
  in	
  40	
  countries	
  
•  60%	
  of	
  members	
  selecAng	
  movies	
  based	
  on	
  
recommendaAons	
  (September	
  2008)	
  
•  NeSlix	
  Prize:	
  US$	
  1,000,000	
  was	
  given	
  to	
  the	
  BellKor's	
  
PragmaAc	
  Chaos	
  team	
  which	
  bested	
  NeSlix's	
  own	
  
algorithm	
  for	
  predicAng	
  raAngs	
  by	
  10.06%	
  (September	
  
2009)	
  
•  75%	
  of	
  the	
  content	
  watched	
  on	
  the	
  service	
  comes	
  
from	
  its	
  recommendaAon	
  engine	
  (April	
  2012)	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  3	
  
Why	
  Recommender	
  Systems	
  
•  Standard	
  uses:	
  
–  Increase	
  the	
  number	
  of	
  items	
  sold	
  
–  Sell	
  more	
  diverse	
  items	
  
–  Increase	
  the	
  user	
  saAsfacAon	
  
–  Increase	
  user	
  fidelity	
  
–  Beeer	
  understand	
  what	
  the	
  user	
  wants	
  
	
  
	
  
•  Advanced	
  uses:	
  
–  Create	
  ad	
  hoc	
  campaigns	
  (per	
  geographic	
  area,	
  per	
  type	
  of	
  users)	
  
–  OpAmize	
  products	
  distribuAon	
  over	
  a	
  wide	
  area	
  for	
  large	
  retail	
  chains	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  4	
  
Problem	
  
•  There	
  are	
  no	
  available	
  sofware	
  products	
  for	
  state-­‐of-­‐
the-­‐art	
  recommender	
  systems	
  
•  There	
  is	
  no	
  "best	
  soluAon"	
  
•  There	
  is	
  no	
  "one	
  soluAon	
  fits	
  all”	
  
•  The	
  NeSlix	
  winner	
  composed	
  104	
  different	
  algorithms	
  
•  A	
  high-­‐end	
  recommender	
  engine	
  can	
  be	
  built	
  only	
  
through	
  expensive	
  custom	
  projects	
  
•  Large	
  scale	
  user/item	
  datasets	
  require	
  a	
  big	
  data	
  
approach	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  5	
  
SoluAon:	
  Reco4J	
  
	
  
A	
  graph-­‐based	
  
recommender	
  engine	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  6	
  
Reco4J	
  Main	
  Goals	
  
•  Implement	
  the	
  state-­‐of-­‐the-­‐art	
  in	
  the	
  recommendaAon	
  
on	
  top	
  of	
  a	
  graph	
  model	
  
•  Ready	
  to	
  use	
  framework	
  
•  Extend/Improve	
  exisAng	
  sofwares:	
  
–  Neo4j	
  
–  ElasAcsearch	
  
–  R	
  
•  Provide	
  sofware	
  /	
  cloud	
  services	
  /	
  consultancy	
  	
  
•  Contribute	
  to	
  the	
  RecSys	
  research	
  field	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  7	
  
Reco4J	
  Features	
  
•  Core	
  
–  Based	
  on	
  collabora.ve	
  filtering	
  approach	
  
–  Independent	
  from	
  source	
  knowledge	
  datasets	
  
–  Persistent	
  models	
  (mulA	
  model	
  supported)	
  
–  Updatable	
  models	
  
–  Composable	
  models/algorithms	
  
•  Algorithms	
  
–  Commercial	
  and	
  research-­‐oriented	
  algorithms	
  
–  Context-­‐aware	
  recommendaAons	
  
–  Social	
  recommendaAons	
  
•  Opera.ons	
  
–  Cluster	
  and	
  cloud-­‐ready	
  for	
  Big	
  Data	
  Analysis	
  
–  MulAtenant	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  8	
  
Reco4J	
  Under	
  the	
  Hood	
  
•  J	
  is	
  for	
  Java	
  
•  Customized	
  algorithm	
  implementaAon	
  based	
  on	
  graph	
  data	
  model	
  
•  Terracoea®	
  Big	
  Memory	
  integraAon	
  
•  Neo4J	
  graph	
  database:	
  
–  Data	
  source	
  repository	
  
–  Persistent	
  model	
  repository	
  
•  Apache	
  Hadoop	
  
–  Map	
  /	
  Reduce	
  based	
  model	
  building	
  
•  Apache	
  Mahout	
  
–  Graph	
  data	
  model	
  
–  Recommender	
  
–  AlternaAng	
  Least	
  Square	
  Algorithms	
  (Hadoop	
  Version)	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  9	
  
Algorithms	
  Roadmap	
  
•  CollaboraAve	
  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	
  Ame)	
  
–  Model	
  based	
  (Latent	
  factor)	
  
•  Stochas6c	
  gradient	
  descendant	
  
•  Alterna6ng	
  least	
  square	
  
•  SVD++	
  (by	
  Koren)	
  
•  Social	
  recommendaAon	
  
–  Trust	
  based	
  approach	
  
–  ProbabilisAc	
  approach	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  10	
  
Algorithms	
  Roadmap	
  (2)	
  
•  Cross-­‐curng	
  features	
  (all	
  algos)	
  
– Context	
  awareness	
  
– Composability	
  
– Real	
  Ame	
  
– ParallelizaAon	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  11	
  
Context-­‐Aware	
  RecommendaAon	
  
“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	
  informa6on	
  in	
  the	
  recommendaAon	
  process	
  
•  Modeling	
  contextual	
  InformaAon	
  
–  From:	
  User	
  x	
  Item	
  -­‐>	
  RaAng	
  
–  To:	
  User	
  x	
  Item	
  x	
  Context	
  -­‐>	
  RaAng	
  
•  Hierarchical	
  structure	
  
•  Three	
  approaches	
  
–  Contextual	
  pre-­‐filtering	
  
–  Contextual	
  post-­‐filtering	
  
–  Contextual	
  modeling	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  12	
  
Advantage	
  of	
  graph	
  database	
  
•  NoSQL	
  database	
  to	
  handle	
  BigData	
  
•  Extensibility	
  
•  No	
  aggregate-­‐oriented	
  database	
  
•  Minimal	
  informaAon	
  needed	
  
•  Natural	
  way	
  for	
  represenAng	
  connecAons:	
  
–  User	
  -­‐	
  to	
  -­‐	
  item	
  
–  Item	
  -­‐	
  to	
  -­‐	
  item	
  
–  User	
  -­‐	
  to	
  -­‐	
  User	
  
•  Graph	
  Based/Social	
  Algorithms	
  
•  Graph	
  ParAAoning	
  (sharding)	
  
•  Performance	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  13	
  
Example:	
  Find	
  Neighbors	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  14	
  
Why	
  Neo4J?	
  
•  Java	
  based	
  
•  Embeddable/Extensible	
  
•  NaAve	
  graph	
  storage	
  with	
  naAve	
  graph	
  processing	
  
engine	
  
•  Open	
  Source,	
  with	
  commercial	
  version	
  
•  Property	
  Graph	
  
•  ACID	
  support	
  
•  Scalability/HA	
  
•  Comprehensive	
  query/traversal	
  opAons	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  15	
  
RecommendaAon	
  Model	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  16	
  
Persistence	
  Model	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  17	
  
Persistence	
  Model	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  18	
  
Persistence	
  Model	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  19	
  
A	
  code	
  example	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  20	
  
Reco4J	
  +	
  Hadoop	
  
•  Queue	
  Based	
  Process	
  
•  Operates	
  both	
  on	
  cluster	
  and	
  cloud	
  
•  Each	
  process	
  downloads	
  data	
  from	
  
Neo4J/Reco4J	
  before	
  or	
  during	
  
computaAon	
  
•  Stores	
  data	
  into	
  Reco4J	
  Model	
  
	
  
•  Scaling	
  augmenAng	
  the	
  number	
  of:	
  
•  Neo4J	
  Nodes	
  (only	
  one	
  master)	
  
•  Hadoop	
  Nodes	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  21	
  
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	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  22	
  
Consultancy	
  
Goals	
  
Analysis	
  
Data	
  
Source	
  
ExploraAon	
  
Process	
  
DefiniAon	
  
Import	
  
Data	
  
Test/
EvaluaAon	
  
Deploy	
  
Alessandro	
  Negro	
   Reco4J	
  Project	
  @	
  London	
  Meetup	
  	
  -­‐	
  June	
  2013	
   Page	
  23	
  
Thank	
  you	
  
Alessandro	
  Negro	
  
Linkedin:	
  hep://it.linkedin.com/in/alessandronegro/	
  
Email:	
  alenegro81@gmail.com	
  
	
  
	
  
	
  
Reco4J	
  
Site:	
  hep://www.reco4j.org	
  
Twieer:	
  @reco4j	
  
GitHub:	
  heps://github.com/reco4j	
  

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Reco4J @ London Meetup (June 26th)

  • 1. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Reco4J  Project   Intelligent  RecommendaAons  for   Your  Business  
  • 2. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  1   Recommender  Systems   •  A  system  that  can  recommend  or  present  items   to  the  user  based  on  the  user’s  interests  and   interacAons   •  One  of  the  best  ways  to  provide  a  personalized   customer  experience   •  Built  by  exploiAng  collecAve  intelligence  to   perform  predicAons   •  Examples:  Amazon,  YouTube,  NeSlix,  Yahoo,   Tripadvisor,  Last.fm,  IMDb  
  • 3. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  2   The  Example:  NeSlix   •  The  world  largest  online  movie  rental  services,  33   million  members  in  40  countries   •  60%  of  members  selecAng  movies  based  on   recommendaAons  (September  2008)   •  NeSlix  Prize:  US$  1,000,000  was  given  to  the  BellKor's   PragmaAc  Chaos  team  which  bested  NeSlix's  own   algorithm  for  predicAng  raAngs  by  10.06%  (September   2009)   •  75%  of  the  content  watched  on  the  service  comes   from  its  recommendaAon  engine  (April  2012)  
  • 4. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  3   Why  Recommender  Systems   •  Standard  uses:   –  Increase  the  number  of  items  sold   –  Sell  more  diverse  items   –  Increase  the  user  saAsfacAon   –  Increase  user  fidelity   –  Beeer  understand  what  the  user  wants       •  Advanced  uses:   –  Create  ad  hoc  campaigns  (per  geographic  area,  per  type  of  users)   –  OpAmize  products  distribuAon  over  a  wide  area  for  large  retail  chains  
  • 5. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  4   Problem   •  There  are  no  available  sofware  products  for  state-­‐of-­‐ the-­‐art  recommender  systems   •  There  is  no  "best  soluAon"   •  There  is  no  "one  soluAon  fits  all”   •  The  NeSlix  winner  composed  104  different  algorithms   •  A  high-­‐end  recommender  engine  can  be  built  only   through  expensive  custom  projects   •  Large  scale  user/item  datasets  require  a  big  data   approach  
  • 6. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  5   SoluAon:  Reco4J     A  graph-­‐based   recommender  engine  
  • 7. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  6   Reco4J  Main  Goals   •  Implement  the  state-­‐of-­‐the-­‐art  in  the  recommendaAon   on  top  of  a  graph  model   •  Ready  to  use  framework   •  Extend/Improve  exisAng  sofwares:   –  Neo4j   –  ElasAcsearch   –  R   •  Provide  sofware  /  cloud  services  /  consultancy     •  Contribute  to  the  RecSys  research  field  
  • 8. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  7   Reco4J  Features   •  Core   –  Based  on  collabora.ve  filtering  approach   –  Independent  from  source  knowledge  datasets   –  Persistent  models  (mulA  model  supported)   –  Updatable  models   –  Composable  models/algorithms   •  Algorithms   –  Commercial  and  research-­‐oriented  algorithms   –  Context-­‐aware  recommendaAons   –  Social  recommendaAons   •  Opera.ons   –  Cluster  and  cloud-­‐ready  for  Big  Data  Analysis   –  MulAtenant  
  • 9. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  8   Reco4J  Under  the  Hood   •  J  is  for  Java   •  Customized  algorithm  implementaAon  based  on  graph  data  model   •  Terracoea®  Big  Memory  integraAon   •  Neo4J  graph  database:   –  Data  source  repository   –  Persistent  model  repository   •  Apache  Hadoop   –  Map  /  Reduce  based  model  building   •  Apache  Mahout   –  Graph  data  model   –  Recommender   –  AlternaAng  Least  Square  Algorithms  (Hadoop  Version)  
  • 10. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  9   Algorithms  Roadmap   •  CollaboraAve  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  Ame)   –  Model  based  (Latent  factor)   •  Stochas6c  gradient  descendant   •  Alterna6ng  least  square   •  SVD++  (by  Koren)   •  Social  recommendaAon   –  Trust  based  approach   –  ProbabilisAc  approach  
  • 11. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  10   Algorithms  Roadmap  (2)   •  Cross-­‐curng  features  (all  algos)   – Context  awareness   – Composability   – Real  Ame   – ParallelizaAon  
  • 12. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  11   Context-­‐Aware  RecommendaAon   “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  informa6on  in  the  recommendaAon  process   •  Modeling  contextual  InformaAon   –  From:  User  x  Item  -­‐>  RaAng   –  To:  User  x  Item  x  Context  -­‐>  RaAng   •  Hierarchical  structure   •  Three  approaches   –  Contextual  pre-­‐filtering   –  Contextual  post-­‐filtering   –  Contextual  modeling  
  • 13. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  12   Advantage  of  graph  database   •  NoSQL  database  to  handle  BigData   •  Extensibility   •  No  aggregate-­‐oriented  database   •  Minimal  informaAon  needed   •  Natural  way  for  represenAng  connecAons:   –  User  -­‐  to  -­‐  item   –  Item  -­‐  to  -­‐  item   –  User  -­‐  to  -­‐  User   •  Graph  Based/Social  Algorithms   •  Graph  ParAAoning  (sharding)   •  Performance  
  • 14. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  13   Example:  Find  Neighbors  
  • 15. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  14   Why  Neo4J?   •  Java  based   •  Embeddable/Extensible   •  NaAve  graph  storage  with  naAve  graph  processing   engine   •  Open  Source,  with  commercial  version   •  Property  Graph   •  ACID  support   •  Scalability/HA   •  Comprehensive  query/traversal  opAons  
  • 16. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  15   RecommendaAon  Model  
  • 17. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  16   Persistence  Model  
  • 18. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  17   Persistence  Model  
  • 19. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  18   Persistence  Model  
  • 20. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  19   A  code  example  
  • 21. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  20   Reco4J  +  Hadoop   •  Queue  Based  Process   •  Operates  both  on  cluster  and  cloud   •  Each  process  downloads  data  from   Neo4J/Reco4J  before  or  during   computaAon   •  Stores  data  into  Reco4J  Model     •  Scaling  augmenAng  the  number  of:   •  Neo4J  Nodes  (only  one  master)   •  Hadoop  Nodes  
  • 22. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  21   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  
  • 23. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  22   Consultancy   Goals   Analysis   Data   Source   ExploraAon   Process   DefiniAon   Import   Data   Test/ EvaluaAon   Deploy  
  • 24. Alessandro  Negro   Reco4J  Project  @  London  Meetup    -­‐  June  2013   Page  23   Thank  you   Alessandro  Negro   Linkedin:  hep://it.linkedin.com/in/alessandronegro/   Email:  alenegro81@gmail.com         Reco4J   Site:  hep://www.reco4j.org   Twieer:  @reco4j   GitHub:  heps://github.com/reco4j