Improving User Drofiles for E-Commerce by GeneSic AlgoriShms 33 tance b. Avertge sttEsftctEon l ig. fi. The sys&em improvemens onIn recommends tae top loo Eems for sers tnd tlso onln rece Fes feedbtcks tbont. L00 Rems from ners, waere tae set of feedbtck Fems tnd tae set of recommended fems tre not ident ectl. In otaer words. Ga needs to setrca for Edetl nser proPles btsed on 2% of EformttEn T aese resnlts snggest tatt onr leting mecatnEm s efficient tnd ctn grettIn improve nser proPles. SEce GA onIn ats 2% form tEn for letrnEng, tae IFnEed Fmformtt En lEgat restrict tae letrnmg tbHEn Tahs problem wts tIso observed mn tae xper Ments. In i gore 2, tae tccrmtcn of wlsa-IFts slkgatIn decretses tfter 50 generttEons of letrnEmg. Tae retson Es tatt tae letrng mecatnEsm constr五edb l五 nEed feedbt cks.ToⅢ strate der tae follow五 extmple. AssIme nser-IEes most of tae Eems recommended bn expert However bectnse tae recommendtthn sistem onln recommends few fems nd nser- does not atve tae catnce to know expert As recommendtt Eons tae letrnEmg mecatnEsm ats no thIn to discover tae reltt Eonsarp between A tnd -.Genertlln tals problem ctn be tllev It ted bn sever tl evolnt Eons. 一a Generations l ig. 3. Impacs of noise In order to comptre tae sustem per formtnce waen tae nser relevtnce feed btcks tre imperfect, we trodnce Pve dEferent nole levels n tae experments❙❯❢❣❧❛❩❇❥✣s✦❵❚♥❨✡r✐❞q❳P❩ ☎❩❇❥✍➦❉③ ❳✽❞✟✉④❥✍❩✸❽❁❸❡❤✕❥❦❢❣❢❣❳P❩❇♦✽❳✸➝❲❝✮➺✐❳✽❚♥❳P❱❇❵ ♦✖⑦❍③❨❦❥✍❩❇❵❱❇✇♥❢❣❞ ￾☞✝ 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 20 40 60 80 100 120 Generations Similarity Distance 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 20 40 60 80 100 120 Generations Average Satisfication ❏❊ä▲❆❉❃✃✵❃ Ï④❏✦◆P❃❒❇▼➧❑⑧❃④◗❇❒✽❏✦❋▲➮✣■ ❘✂ä ÞÒ❛■✍◆✽❏✦Ð❛■➽◗P❏❲❒P❃④◗❇❮❾❏❛➮❫❒P❃➱❛❋ ￾✂✁✄ ➙✁￾❊➙ ➠②✇♥❳✱❞❡❝❛❞❡❱❇❳✽❢ ❵❢❣❧❛❩❇❥✣st❳✽❢❣❳✽❚❲❱ ➱❛❋❊Ï▼✧◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊◗✕❒P❈❊■✸❒P➱❛ß ✱✶❭❉❭✡❃❒P■✍✃①◗✟❮➊➱❛◆✟❖▲◗❭■✍◆✽◗✟❏✦❋▲❑✵❏✦Ï④◗❭➱➽➱❛❋❊Ï▼✧◆P■❦➮✣■✍❃Ò❛■❦◗✕❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó⑧◗ ❏✦❘➩➱❛❖⑧❒❲✱✶❭❉❭➢❃❒P■✍✃①◗✡❮➊◆P➱❛✃✩❖▲◗❭■✍◆✽◗✍❐➩ç✱❈❊■✍◆P■✧❒P❈❊■①◗❭■✣❒❣➱✦❮✼❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó✿❃❒P■✍✃①◗❣❏✦❋▲❑✿❒P❈❊■①◗❭■✣❒❣➱✦❮ ◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑⑧■❦❑✮❃❒P■✍✃①◗✐❏✦◆P■✸❋❊➱✦❒❍❃④❑⑧■✍❋♥❒P❃④➮✍❏✦Ï❯ä❛èq❋①➱✦❒P❈❊■✍◆❍ç✐➱❛◆✽❑❊◗✍❐❉✮Þ ❋❊■✍■❦❑❊◗②❒P➱✧◗❭■❦❏✦◆✽➮✽❈✧❮➊➱❛◆ ❃④❑⑧■❦❏✦Ï▲❖▲◗❭■✍◆✼ß❊◆P➱✥￾▲Ï■❦◗✟❘▲❏❛◗❭■❦❑✮➱❛❋ ✷ ✄ ➱✦❮❁❃❋⑧❮➊➱❛◆P✃①❏❲❒P❃➱❛❋✂ä❛æ✸❈❊■❦◗❭■✖◆P■❦◗❭❖❊Ï❒✽◗✼◗❭❖❊Ð❛Ð❛■❦◗❇❒✟❒P❈▲❏❲❒❍➱❛❖❊◆ Ï■❦❏✦◆P❋❊❃❋❊Ð✵✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃ ❃④◗✸■✣ã➢➮✣❃■✍❋♥❒✡❏✦❋▲❑☞➮✍❏✦❋☞Ð❛◆P■❦❏❲❒PÏ▼➢❃✃✵ß❊◆P➱❲Ò❛■❄❖▲◗❭■✍◆✱ß❊◆P➱✥￾▲Ï■❦◗✍ä ❆❉❃❋▲➮✣■❲✮Þ ➱❛❋❊Ï▼✿❈▲❏❛◗✭✷ ✄ ❃❋⑧❮➊➱❛◆P✃①❏❲❒P❃➱❛❋✿❮➊➱❛◆➽Ï■❦❏✦◆P❋❊❃❋❊Ð▲❐❊❒P❈❊■✵Ï ❃✃✵❃❒P■❦❑✢❃❋⑧❮➊➱❛◆P✃①❏❲❒P❃➱❛❋ ✃✵❃Ð❛❈♥❒➢◆P■❦◗❇❒P◆P❃④➮❫❒✵❒P❈❊■✿Ï■❦❏✦◆P❋❊❃❋❊Ð ❏✦❘❊❃ Ï ❃❒❇▼❛ä❍æ✸❈❊❃④◗✵ß❊◆P➱❛❘❊Ï■✍✃✺ç✸❏❛◗①❏✦Ï④◗❭➱ ➱❛❘▲◗❭■✍◆PÒ❛■❦❑ ❃❋ö❒P❈❊■ ■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒✽◗✍ä✂èq❋ ❃✕❃Ð❛❖❊◆P■ ✷⑧❐❁❒P❈❊■➢❏❛➮✍➮✣❖❊◆✽❏❛➮✣▼✿➱✦❮✱ç✱❃④◗❭❈⑧❅❡Ï ❃④◗❇❒✽◗❣◗❭Ï ❃Ð❛❈♥❒PÏ▼á❑⑧■❦➮✣◆P■❦❏❛◗❭■❦◗➽❏❲❮➀❒P■✍◆ ✁ ❭ Ð❛■✍❋❊■✍◆✽❏❲❒P❃➱❛❋▲◗①➱✦❮✧Ï■❦❏✦◆P❋❊❃❋❊Ð▲ä✐æ✸❈❊■✢◆P■❦❏❛◗❭➱❛❋✫❃④◗➢❒P❈▲❏❲❒☞❒P❈❊■áÏ■❦❏✦◆P❋❊❃❋❊Ð ✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃ ❃④◗ ➮✣➱❛❋▲◗❇❒P◆✽❏✦❃❋❊■❦❑ö❘❉▼ ❒P❈❊■åÏ ❃✃✵❃❒P■❦❑⑩❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó⑧◗✍ä✼æ✈➱ ❃ Ï Ï❖▲◗❇❒P◆✽❏❲❒P■❛❐❍➮✣➱❛❋▲◗❭❃④❑⑧■✍◆①❒P❈❊■✿❮➊➱❛Ï Ï➱❲ç✱❃❋❊Ð ■✣é❊❏✦✃✵ß❊Ï■❛ä Þ◗P◗❭❖❊✃✵■❣❖▲◗❭■✍◆ ✂ Ï ❃Ó❛■❦◗✱✃✵➱♥◗❇❒✱➱✦❮✕❒P❈❊■✛❃❒P■✍✃①◗✱◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑⑧■❦❑☞❘❉▼➧■✣é⑧ß➩■✍◆❭❒✢✚✛ä ✦✖➱❲ç✐■✍Ò❛■✍◆❦❐❁❘➩■❦➮✍❏✦❖▲◗❭■①❒P❈❊■➢◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋à◗❭▼⑧◗❇❒P■✍✃✓➱❛❋❊Ï▼✢◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊◗➽❮➊■✍ç ❃❒P■✍✃①◗ ❏✦❋▲❑✝❖▲◗❭■✍◆✁✂➫❑⑧➱❉■❦◗✱❋❊➱✦❒✡❈▲❏tÒ❛■➽❒P❈❊■✧➮✽❈▲❏✦❋▲➮✣■➽❒P➱➢Ó❉❋❊➱❲ç ■✣é⑧ß➩■✍◆❭❒ ✚✛ü ◗✖◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋▲◗✍❐ ❒P❈❊■①Ï■❦❏✦◆P❋❊❃❋❊Ð☞✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃✩❈▲❏❛◗❄❋❊➱✿❏✦❘❊❃ Ï ❃❒❇▼✿❒P➱å❑⑧❃④◗P➮✣➱❲Ò❛■✍◆✡❒P❈❊■①◆P■✍Ï④❏❲❒P❃➱❛❋▲◗❭❈❊❃ß✢❘➩■✣❒❇ç✐■✍■✍❋ ✚ ❏✦❋▲❑ ✂✮ä✆✮❄■✍❋❊■✍◆✽❏✦Ï Ï▼❛❐❉❒P❈❊❃④◗✱ß❊◆P➱❛❘❊Ï■✍✃ ➮✍❏✦❋☞❘➩■✛❏✦Ï Ï■✍Ò❉❃④❏❲❒P■❦❑➧❘❉▼➧◗❭■✍Ò❛■✍◆✽❏✦Ï➤■✍Ò❛➱❛Ï❖⑧❒P❃➱❛❋▲◗✍ä 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 70 80 90 100 Generations Similarity Distance No Noise Level of Noise=1 Level of Noise=2 Level of Noise=3 Level of Noise=4 ￾✂✁✄ ➙✄✂❊➙ ❙❯❢❣❧❉❬✍♦P❱✟❥✍✉❁❚♥❥❦❵ ❞q❳ èq❋✛➱❛◆✽❑⑧■✍◆✈❒P➱❄➮✣➱❛✃✵ß▲❏✦◆P■✼❒P❈❊■✸◗❭▼⑧◗❇❒P■✍✃ ß➩■✍◆❭❮➊➱❛◆P✃①❏✦❋▲➮✣■❍ç✱❈❊■✍❋✛❒P❈❊■✐❖▲◗❭■✍◆✕◆P■✍Ï■✍Ò❲❏✦❋▲➮✣■✼❮➊■✍■❦❑❉❅ ❘▲❏❛➮✽Ó⑧◗✕❏✦◆P■❍❃✃✵ß➩■✍◆❭❮➊■❦➮❫❒❦❐❲ç✐■✐❃❋♥❒P◆P➱⑧❑⑧❖▲➮✣■✑￾▲Ò❛■✸❑⑧❃â➤■✍◆P■✍❋♥❒②❋❊➱❛❃④◗❭■✐Ï■✍Ò❛■✍Ï④◗✈❃❋✧❒P❈❊■✐■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒✽◗