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ERCIM	
  research	
  project	
  at	
  CWI	
  	
  
Focused	
  on	
  novel	
  aspects	
  of	
  recommender	
  systems	
  
evalua3on	
  from	
  an	
  informa3on	
  retrieval	
  and	
  a	
  user-­‐
centric	
  perspec3ve.	
  
	
  
Dura3on	
  and	
  personnel	
  
§  January	
  2013	
  –	
  March	
  2014	
  
§  24	
  person	
  months	
  
§  2	
  postdocs	
  
	
  
Centrum	
  Wiskunde	
  &	
  Informa:ca 	
  	
   	
   	
   	
  	
  	
  	
  	
  	
  	
  	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
   	
  www.cwi.nl	
  
Alan	
  Said,	
  Alejandro	
  Bellogín,	
  Arjen	
  De	
  Vries,	
  Benjamin	
  Kille	
  
{alan.said,	
  alejandro.bellogin,	
  arjen.de.vries}@cwi.nl,	
  kille@dai-­‐lab.de	
  
Informa3on	
  Retrieval	
  and	
  User-­‐Centric	
  
Recommender	
  System	
  Evalua3on	
  
	
  
UMAP	
  2013	
  –	
  Rome,	
  Italy	
  
Acknowledgement	
  
	
  
This	
  work	
  is	
  carried	
  out	
  during	
  the	
  
tenure	
  of	
  an	
  ERCIM	
  “Alain	
  
Bensoussan”	
  Fellowship	
  Programme.	
  
The	
  research	
  leading	
  to	
  these	
  results	
  
has	
  received	
  funding	
  from	
  the	
  
European	
  Union	
  Seventh	
  Framework	
  
Programme	
  (FP7/2007-­‐2013)	
  under	
  
grant	
  agreement	
  no.246016.	
  	
  
User-­‐centric	
  Evalua:on	
  	
  
A	
  personalized	
  offline	
  evalua:on	
  protocol	
  mimicking	
  real-­‐life	
  
deployed	
  recommender	
  systems.	
  
•  Each	
  algorithm	
  is	
  trained	
  once	
  per	
  user	
  based	
  on	
  a	
  	
  
personalized	
  training	
  and	
  test	
  split.	
  
•  Candidate	
  test	
  items	
  are	
  selected	
  based	
  on	
  a	
  personal	
  ra:ng	
  	
  
value	
  threshold	
  (e.g.	
  mean).	
  
	
  
Diversity-­‐oriented	
  evalua:on	
  methods	
  focusing	
  on:	
  
•  non-­‐accuracy-­‐based	
  evalua3on	
  metrics	
  
•  item	
  feature-­‐centric	
  evalua3on	
  
•  novelty	
  
IR-­‐based	
  Evalua:on	
  	
  
A	
  coherence-­‐based	
  func:on	
  that	
  is	
  able	
  to	
  predict	
  the	
  user	
  performance	
  
•  Correlated	
  with	
  the	
  “magic	
  barrier”	
  
•  Improves	
  the	
  total	
  performance	
  of	
  the	
  system	
  when	
  used	
  to	
  group	
  
users	
  into	
  difficult	
  and	
  easy	
  ones	
  
	
  
Explore	
  correla:ons	
  between	
  metrics	
  typically	
  used	
  in	
  offline	
  experiments	
  
(precision)	
  and	
  in	
  real	
  applica3ons	
  (e.g.,	
  CTR).	
  
	
  
Perform	
  an	
  analysis	
  of	
  how	
  evalua3on	
  in	
  recommenda3on	
  should	
  be	
  
performed	
  to	
  obtain	
  interpretable	
  and	
  comparable	
  results.	
  
	
  
Related	
  Events	
  	
  
Workshop	
  on	
  Reproducibility	
  and	
  Replica1on	
  in	
  Recommender	
  
Systems	
  Evalua1on.	
  In	
  conjunc3on	
  with	
  RecSys	
  2013	
  
	
  
	
  Workshop	
  on	
  Benchmarking	
  Adap1ve	
  Retrieval	
  and	
  Recommender	
  
Systems.	
  In	
  conjunc3on	
  with	
  SIGIR	
  2013	
  
	
  
	
  
ACM	
  TIST	
  Special	
  Issue	
  on	
  Recommender	
  System	
  Benchmarking	
  
Further	
  Reading	
  	
  
Ar1st	
  popularity:	
  do	
  web	
  and	
  social	
  music	
  services	
  agree?	
  	
  
A.	
  Bellogín	
  et	
  al.	
  In	
  ICWSM	
  2013	
  
	
  
	
  
	
  
User-­‐Centric	
  Evalua1on	
  of	
  a	
  K-­‐Furthest	
  Neighbor	
  Collabora1ve	
  Filtering	
  	
  
Recommender	
  Algorithm	
  	
  
A.	
  Said	
  et	
  al.	
  in	
  CSCW	
  2013	
  
	
  
Poster	
  Abstract	
  
Informa1on	
  Retrieval	
  and	
  User-­‐Centric	
  Recommender	
  	
  
System	
  Evalua1on	
  
A.	
  Said,	
  A.	
  Bellogín	
  et	
  al.	
  in	
  UMAP	
  2013	
  
	
  
	
  

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Information Retrieval and User-centric Recommender System Evaluation

  • 1. ERCIM  research  project  at  CWI     Focused  on  novel  aspects  of  recommender  systems   evalua3on  from  an  informa3on  retrieval  and  a  user-­‐ centric  perspec3ve.     Dura3on  and  personnel   §  January  2013  –  March  2014   §  24  person  months   §  2  postdocs     Centrum  Wiskunde  &  Informa:ca                                                      www.cwi.nl   Alan  Said,  Alejandro  Bellogín,  Arjen  De  Vries,  Benjamin  Kille   {alan.said,  alejandro.bellogin,  arjen.de.vries}@cwi.nl,  kille@dai-­‐lab.de   Informa3on  Retrieval  and  User-­‐Centric   Recommender  System  Evalua3on     UMAP  2013  –  Rome,  Italy   Acknowledgement     This  work  is  carried  out  during  the   tenure  of  an  ERCIM  “Alain   Bensoussan”  Fellowship  Programme.   The  research  leading  to  these  results   has  received  funding  from  the   European  Union  Seventh  Framework   Programme  (FP7/2007-­‐2013)  under   grant  agreement  no.246016.     User-­‐centric  Evalua:on     A  personalized  offline  evalua:on  protocol  mimicking  real-­‐life   deployed  recommender  systems.   •  Each  algorithm  is  trained  once  per  user  based  on  a     personalized  training  and  test  split.   •  Candidate  test  items  are  selected  based  on  a  personal  ra:ng     value  threshold  (e.g.  mean).     Diversity-­‐oriented  evalua:on  methods  focusing  on:   •  non-­‐accuracy-­‐based  evalua3on  metrics   •  item  feature-­‐centric  evalua3on   •  novelty   IR-­‐based  Evalua:on     A  coherence-­‐based  func:on  that  is  able  to  predict  the  user  performance   •  Correlated  with  the  “magic  barrier”   •  Improves  the  total  performance  of  the  system  when  used  to  group   users  into  difficult  and  easy  ones     Explore  correla:ons  between  metrics  typically  used  in  offline  experiments   (precision)  and  in  real  applica3ons  (e.g.,  CTR).     Perform  an  analysis  of  how  evalua3on  in  recommenda3on  should  be   performed  to  obtain  interpretable  and  comparable  results.     Related  Events     Workshop  on  Reproducibility  and  Replica1on  in  Recommender   Systems  Evalua1on.  In  conjunc3on  with  RecSys  2013      Workshop  on  Benchmarking  Adap1ve  Retrieval  and  Recommender   Systems.  In  conjunc3on  with  SIGIR  2013       ACM  TIST  Special  Issue  on  Recommender  System  Benchmarking   Further  Reading     Ar1st  popularity:  do  web  and  social  music  services  agree?     A.  Bellogín  et  al.  In  ICWSM  2013         User-­‐Centric  Evalua1on  of  a  K-­‐Furthest  Neighbor  Collabora1ve  Filtering     Recommender  Algorithm     A.  Said  et  al.  in  CSCW  2013     Poster  Abstract   Informa1on  Retrieval  and  User-­‐Centric  Recommender     System  Evalua1on   A.  Said,  A.  Bellogín  et  al.  in  UMAP  2013