早大学位記番号:新6414早稲田大

LI Xiaohui

English

早稲田大学博士(工学)早大学位記番号:新6414doctoral thesi

李, 暁慧

57121

LI, Xiaohui

Waseda University Repository

 ???????????????????     ?? ?? ?? ?? ?? ?         ? ? ? ? ? ? ?   In f o rma t i on  Ex tra c t i on  Met hod  f o r  C aptu r in g  User  P re fe ren ces  an d  Re commen der  Sy s tem  A pp l i ca t i on         ?? ? ?? ? ?  Xiaohui LI  ???????????? ????????      2013 ? 4 ?  2     Recommender  systems  are  becoming  a n indispensable  appl icat ion  and  re -shaping  the  wor ld  in  e - commerce  s copes .  Recommender  sys tems  are  pr imar i ly  d i rec ted  towards  ind iv idua ls  who lack  suf f i c i ent  persona l  exper ience  to  eva luate  the  potent ia l ly  overwhelming number  o f  a l ternat ive  i t ems.  With the  explos ive  growth  and  var ie ty  o f  in format ion  avai lab le  on the  Internet ,  a  press ing  need emerged  for  prov id ing  recommendat ions  der ived  f rom f i l ter ing  the  whole  range  o f  ava i lable  a l ternat ives .  Personal i zed  recommender  systems have been cons idered to  be  one  o f  the  most  promis ing approaches  f or  the  ind iv idua l i zed  serv ices ,  inc lud ing  try  to  pred i ct  what  the  most  su itab le  products  or  serv ices  based  on the  user ’s  preferences  and prov ide  the  suggest ions  for  an  ind iv idual  user.       Capturing  user  preferences  is  a  cruc ia l  task  in  recommender  sys tems .  Simply  ask ing  the  users  what  they  want  is  too  intrus ive  and prone  to  error,  and  then  f inding  meaningfu l  pat terns  i s  d i f f i cu lt .  The  ex istence  o f  a  user  mode l  i s  centra l  to  every recommender  sys tem;  the  way  in  which  th is  informat ion is  acquired  and exp lo i t ed  depends  on  the  part i cular  recommendat ion technique.  Capturing  accurate  user  pre ferences  i s  however,  an essent ia l  task  i f  the  recommender  systems are  to  respond dynamica l ly  to  the  changing needs  o f  thei r  users .        A lthough many a lgor i thms proposed in  recommender  sys tems l i t erature  have  grounded  in  the  prob lem domain  o f  recommending academic  research es ,  a  wel l -known prob lem ,  co ld -s tar t  recommendat ion is  s t i l l  seek to  so lve .  Furthermore ,  a  recent  and notab le  quest ion in  this  research  area  focuses  on  how to  take  into  account  not  on ly  in format ion  about  users  and products  but  a lso  incorporat e  contextua l  informat i on into  the  dec is ion -making  env ironment .  Whi le  most  ex is t ing  recommend er  system research  papers  overemphas ize  the  s imi lar i ty  between  the  user  preferences  and the  i tem features  to  improve  the  recommendat ion  accuracy,  the  importance  o f  recommendat ion d ivers i ty  are  o f ten over looked .       To  address  the  a forementioned  is sues ,  th is  d issertat ion  a ims to  des ign  the  innovat ive  recommendat ion methods  take into  cons iderat ion  f rom the  part i cu lar  po int  o f  v iew  for  improv ing the  qua l i ty  o f  recommendat ion.  A track ing  recommendat ion  approach  based  on  Formal  Concept  Analys is  for  new user  and gray  sheep prob lems  in  ex i st ing  r ecommender  systems  i s  introduced .  An af fec t -based recommendat ion  approach  for  e l i c i t ing user ’s  trans i t i ona l  pre ferences  is  presented .  A hybr id  recommendat ion  approach  3 that  increases  recommendat ion d ivers i ty  whi le  main ta ining  an  acceptab le  accuracy l eve l  i s  descr ibed .  To  demonstrate  e f f ec t iveness  o f  the  proposed  approaches ,  a  movie  recommendat ion app l i cat ion  was  i l lus trated  as  a n example .   Moreover,  this  d i sser tat ion employ s  the  comprehens ive  metr i cs  to  ach ieve  eva luat ion o f  recommendat ion qual i ty.  The exper imenta l  result s  show that  the  proposed approach  improved  the  accuracy  and  d ivers i ty  o f  recommendat ion than tradi t i onal  recommendat ion approaches .       The contents  o f  the  thes i s ,  which  cons ist  o f  7  chapters ,  are  introduced as  f o l l ows .       Chapter  1  introduces  the  research background ,  mot ivat ion ,  ob jec t ive ,  and  the  s t ructure  o f  th is  thes is .       Chapter  2  rev iews  the  re lated  research for  th is  work .  This  inc ludes  an in-depth  invest igat ion  on  ex ist ing  recommendat ion  techniques  and thei r  drawbacks  by exa mining the  recommender  sys tems  wi th in  the  l i t erature .       Chapter  3  descr ibes  three  prob lems  to  be  so lved in  thi s  research,  inc luding co ld - start  recommendat ion  problem,  which at tempts  to  present  i t ems that  are  l ikely  o f  in teres t  to  the  new user ;  context -aware  recommendat ion  prob lem,  which  a ims  at  improving accuracy  o f  recommendat ions  by  ta i l or ing  these  to  the  part i cular  context ;  and  d ivers i ty  recommendat ion  prob lem,  which  detec ts  user ’s  c rossover  preferences  to  avo id  prov id ing too  s imi lar  i t ems for  ind iv idual  user.       Chapter  4  presents  a  new track ing recommendat ion approach to  prov ide  the  re levant  i tems  for  new  user  co ld - start  prob lem.  A new recommendat ion mode l  based  the  synerg ist i c  use  o f  in format ion  f rom repos i tory,  which inc ludes  user ’s  f ew  h is tory  records ,  and  i t ems ’ two - leve l  property was  cons tructed .  The hierarchica l  s t ruc ture  map based on the  c lass i f i catory  att r ibute  o f  i tems  generat ing  by  Formal  Concept  Analys is  (FCA)  is  used  to  d is cover  the  candidate  i t ems that  the  user  may be  intereste d .  And  an appropr iate  recommendat ion returned to  new user  ut i l i zes  the  f i l t er ing  techniques  based  on  the  descr ipt ive  at t r ibute  ranking  these  candidates .  The  experimenta l  s tudy  inc ludes  s imulat ing  a  proto type  recommender  system for  implement ing the  proposed approach  and  tes t ing  i ts  per formance .  Exper iments  us ing two  datasets  show our  approach he lp  to  improve  the  prec is ion  and  the  recal l  ra tes  compared  to  4 tradi t iona l  co l laborat ive  f i l ter ing  approac h by  up  to  15.3% and  12.5% respect ive ly  under  new user  co ld -s tart  cond i t ions .       Chapter  5  f ocuses  on exp lor ing an a f fec t -based approach for  context -aware  recommendat ion  prob lem.  The  presented  approach  at tempts  to  catch  users ’ t rans i t i ona l  preferences  and gu ide  t imely  recommendat ions  based  on  the  t ransformat ion  o f  user  preferences .  F irs t ly,  the  conceptual  network  o f  each  i tems  based  on  the ir  descr ipt ive  keywords  are  c reated  us ing  WordNet  and co lor  theory.  Second ly,  co lor  orders  o f  each  i t em are  generate d  v ia  the  spreading  ac t ivat io n.  Third ly,  a  charac ter i s t i c  sequence  cons ist ing o f  co lor  nodes  was  extrac ted  f rom co lor  orders  o f  users ’ record  for  in ferr ing  the  contextua l  informat ion ;  th is  informat ion i s  subsequent ly  used to  perce ive  the  changing  o f  user  preference .  The  performance  o f  the  proposed approach was  i l lus trated  us ing the  example  o f  a  movie  recommendat ion app l icat ion .  The  performance  tes t  resu lt s  show that  the  proposed approac h outperformed approx imate  15% than t rad it i ona l  co l laborat ive  f i l t er ing  approach in  terms  of  accuracy.       Chapter  6  d iscusses  a  new hybr id  approach that  incorporates  mul t id imens ional  c luster ing  into  a  co l laborat ive  f i l t er ing  recommendat ion  process  f or  d ivers i ty  recommendat ion  prob lem.  The  present ed approach  a ims  to  detec t  the  users ’ crossover  pre ferences  f rom divers i f i ca t ion  view  to  meet  the i r  potent ial  demands  and  prov ide  the  target  user  w i th h igh ly  id iosyncrat ic  o r  more  d iverse  recommendat ions .  The pr oposed hybrid  recommendat ion procedure  has  been  part i t i oned into  three  phases :  i )  data  preprocess ing and mult id imens ional  c luster ing ;  i i )  choos ing  the  appropr iate  c lusters ;  i i i )  co l laborat ive  f i l ter ing  recommendat ion process  for  the  target  user.  Convergenc e  and Coverage  are  def ined  as  two pruning cr i ter ion respect ive ly  to  prune  the  ini t ia l  c lusters  set .  The performance  o f  proposed  approach i s  evaluated us ing a  pub l ic  movie  dataset  and compared with two  representat ive  recommendat ion algor i thms.  The comprehens ive  empir ica l  eva luat ion  demonstrates  that  the  proposed approach increas ed 15%-20% divers i ty  as  compared  to  the  t rad it i onal  recommendat ion  approaches  whi le  mainta in ing 75% o f  recommendat ion  accuracy.       Chapter  7  concludes  th is  thes is  by co l lat ing the  ev idence  f or  the  proposed recommendat ion methodo logy  and summariz ing  the i r  advantages .  The future  d i rec t i on  f or  the  work  deta i l ed  with in th is  thes is  i s  then d iscussed .  

Information Extraction Method for Capturing User Preferences and Recommender System Application

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顧客嗜好理解のための情報抽出とそのレコメンダーシステムへの応用

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顧客嗜好理解のための情報抽出とそのレコメンダーシステムへの応用

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