《电子商务 E-business》阅读文献：Improving User Profiles for E-Commerce by Genetic Algorithms

Improving User Profiles for E-Commerce by Genetic algorithms* Yi-Shin Chen and Cyrus Shahab Integrated Media Systems Center and Computer Science Department University of Southern California, Los Angeles, CA 90089-2564 Abstract. Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. The major chal- enge for these systems is bridging the gap between the physical characteristics of data with the users'perceptions. In order to address this challenge, employing user profiles to improve accuracy becomes essential. However, the system performance offer learning mechanisms to correct erroneous user in puts In this paper, we extend an existing recommendation system, Yoda, to improve the profiles au tomatically by utilizing users'relevance feedback with genetic algorithms(GA). Our experimental results indicate that the retrieval accuracy is significantly increased by using the G Keywords: e-commerce, recommendation systems, fuzzy logic, soft query clustering, genetic algorithm, relevance feedback 1 Introduct As the amount of available products in e-commerce businesses is burgeon ng, searching for desired products among enormous offerings is becoming increasingly difficult. As a result, e-commerce users frequently suffer from in formation overload. To alleviate this problem, recommendation systems are being widely adopted to help customers locate products they would like to purchase. In essence, these systems apply data analysis techniques to provide a list of recommended products for each online customer. The most famous example in e-commerce businesses is the"Customers who bought feature used in Amazon. comM, which is basically applied to every product page on its websites. With the help of this feature, Amazon. com s system recom mends similar products to the current buyer based on the purchase histories of previous customers who bought the same product This research has been funded in part by NSF grants EEC-9529152(IMSC ERC) nd itR-0082826. NIH-NLM RO1-LM07061, DARPA and USAF hent nr. F30602-99-1-0524, and unrestricted cash/equipment gifts from NCR, M. Intel and suN

￾✂✁☎✄✝✆✟✞✡✠☞☛✍✌✝✎✑✏✓✒✕✔✖✆✘✗✙✆✟✞✛✚✢✜✣✔✖✒✥✤✦✞✧✆✩★✫✪✂✬✭✞✮✁☎✁✯✔✖✆✟✰✱✔✳✲✵✴ ✶✔✡✌✝✔✸✷✟☛✣✰✺✹✻✜✣✎✧✞✧✆✼☛✽✷✟✾✿✁✯✒❁❀ ❂❄❃ ❅❇❆❉❈❊❃❋✢●❍❈❊■✍❋✿❏✦❋▲❑✿●❍▼❉◆P❖▲◗✖❆❉❈▲❏✦❈▲❏✦❘❊❃ ❙❯❚❲❱❇❳✽❨✍❩❭❬✣❱❇❳❫❪✵❴✮❳❫❪❛❵ ❬✛❜❲❝❛❞❡❱❇❳✽❢❣❞✼❤✕❳✽❚❲❱❇❳P❩✐❬✍❚❉❪✵❤✕❥❦❢❣❧♥♠❛❱❇❳P❩✐❜✦♦✽❵ ❳✽❚♥♦✽❳✱♣✸❳✽❧❉❬✣❩q❱❇❢❣❳✽❚❲❱ r✐❚♥❵st❳P❩❇❞q❵ ❱❯❝✧❥✍✉✈❜✦❥❦♠❛❱❇✇♥❳P❩❇❚①❤②❬✍③ ❵✉④❥✍❩❇❚♥❵ ❬✦⑤▲⑥▲❥❦❞❍⑦❍❚♥❨❦❳✽③ ❳✽❞✽⑤⑧❤②⑦⑩⑨❦❶❦❶❦❷❦⑨✣❸❡❹❦❺✍❻✍❼ ❽❁❸❾❢✛❬✍❵ ③➀❿ ➁ ➂❫➃♥➄❛➃♥➄✦➅P➆q➇❇➈✦➆❯➂❫➃♥➆➊➉❊➋❭➌④➍✐➎❊➂✽➋✍➏ ➐❫➑❲➎ ➒✛➓❁➔✽→✽➣✣↔❉↕❦→❦➙❄➛❳✽♦✽❥❦❢❣❢❣❳✽❚❉❪♥❬✣❱❇❵❥❦❚✖❞❡❝❛❞❡❱❇❳✽❢❣❞❁❬✣❩❇❳✕➜✼❵❪❛❳✽③❝❄❬❦❪❛❥❦❧❛❱❇❳❫❪✖❵❚❄❳P❸❾♦✽❥❦❢❣❢❣❳P❩❇♦✽❳✈➝♥♠♥❞q❵❚♥❳✽❞q❞q❳✽❞ ✉④❥✍❩②✇♥❳✽③❧♥❵❚♥❨✖♦✽♠♥❞❡❱❇❥❦❢❣❳P❩❇❞✈③❥❲♦❫❬✣❱❇❳✐❧❛❩❇❥✦❪❛♠♥♦P❱❇❞✈❱❇✇♥❳P❝✡➜②❥❦♠♥③❪❣③ ❵➞t❳✼❱❇❥✱❧♥♠❛❩❇♦❭✇❉❬✍❞q❳❦➟✦➠②✇♥❳❍❢✛❬❫➡❡❥✍❩②♦❭✇❉❬✍③ ❸ ③ ❳✽❚♥❨❦❳✖✉④❥✍❩✸❱❇✇♥❳✽❞q❳✖❞❡❝❛❞❡❱❇❳✽❢❣❞❍❵ ❞✐➝❛❩❇❵❪❛❨❦❵❚♥❨❣❱❇✇♥❳✖❨t❬✍❧➢➝⑧❳P❱❯➜②❳✽❳✽❚①❱❇✇♥❳✖❧♥✇❲❝❛❞q❵ ♦❫❬✍③➤♦❭✇❉❬✣❩❭❬✍♦P❱❇❳P❩❇❵ ❞❡❱❇❵ ♦✽❞✸❥✍✉ ❪♥❬✣❱❭❬✸➜✼❵❱❇✇❣❱❇✇♥❳❍♠♥❞q❳P❩❇❞✽➥✦❧⑧❳P❩❇♦✽❳✽❧❛❱❇❵❥❦❚♥❞✽➟❲❙❯❚✛❥✍❩❭❪❛❳P❩✕❱❇❥✡❬❦❪♥❪✦❩❇❳✽❞q❞✈❱❇✇♥❵ ❞✕♦❭✇❉❬✍③ ③ ❳✽❚♥❨❦❳❦⑤✦❳✽❢❣❧♥③❥❫❝❛❵❚♥❨✖♠♥❞q❳P❩ ❧❛❩❇❥✍➦❉③ ❳✽❞✐❱❇❥✮❵❢❣❧❛❩❇❥✣st❳✡❬✍♦✽♦✽♠❛❩❭❬✍♦P❝➧➝⑧❳✽♦✽❥❦❢❣❳✽❞✐❳✽❞q❞q❳✽❚❲❱❇❵ ❬✍③➀➟➩➨✐❥❫➜②❳✽st❳P❩❫⑤⑧❱❇✇♥❳❄❞❡❝❛❞❡❱❇❳✽❢➫❧⑧❳P❩q✉④❥✍❩❇❢✛❬✍❚♥♦✽❳ ❢✛❬✽❝✱❪❛❳✽❨✍❩❭❬❦❪❛❳✕❪❛♠♥❳✂❱❇❥✼❵❚❉❬✍♦✽♦✽♠❛❩❭❬✍♦P❝✱❥✍✉♥♠♥❞q❳P❩➤❧❛❩❇❥✍➦❉③ ❳✽❞✽➟✍➠②✇♥❳P❩❇❳P✉④❥✍❩❇❳❦⑤❦❬✍❚✖❳P➭❊❳✽♦P❱❇❵st❳✈❞❡❝❛❞❡❱❇❳✽❢➯❞q✇♥❥❦♠♥③❪ ❥✍➭❊❳P❩✈③ ❳❫❬✣❩❇❚♥❵❚♥❨✸❢❣❳✽♦❭✇❉❬✍❚♥❵ ❞q❢❣❞➩❱❇❥✐♦✽❥✍❩q❩❇❳✽♦P❱✈❳P❩q❩❇❥❦❚♥❳✽❥❦♠♥❞✂♠♥❞q❳P❩✈❵❚♥❧♥♠❛❱❇❞✽➟✍❙❯❚✖❱❇✇♥❵ ❞✂❧❉❬✍❧⑧❳P❩❫⑤✣➜②❳✟❳P➲✦❱❇❳✽❚❉❪ ❬✍❚❄❳P➲❛❵ ❞❡❱❇❵❚♥❨✐❩❇❳✽♦✽❥❦❢❣❢❣❳✽❚❉❪♥❬✣❱❇❵❥❦❚❄❞❡❝❛❞❡❱❇❳✽❢✵⑤❫➳✕❥✦❪♥❬✦⑤✍❱❇❥✸❵❢❣❧❛❩❇❥✣st❳✈❱❇✇♥❳②❧❛❩❇❥✍➦❉③ ❳✽❞✈❬✍♠❛❱❇❥❦❢✛❬✣❱❇❵ ♦❫❬✍③ ③❝❄➝❲❝ ♠❛❱❇❵ ③ ❵ ➵✽❵❚♥❨✡♠♥❞q❳P❩❇❞✽➥t❩❇❳✽③ ❳✽s❦❬✍❚♥♦✽❳❍✉④❳✽❳❫❪❛➝❉❬✍♦❭➞✡➜✼❵❱❇✇✧❨❦❳✽❚♥❳P❱❇❵ ♦✐❬✍③❨❦❥✍❩❇❵❱❇✇♥❢❣❞✸➸❾➺✸⑦✸➻❭➟✦➼✐♠❛❩②❳P➲❛❧⑧❳P❩❇❵❢❣❳✽❚❲❱❭❬✍③ ❩❇❳✽❞q♠♥③❱❇❞✡❵❚❉❪❛❵ ♦❫❬✣❱❇❳➽❱❇✇❉❬✣❱✖❱❇✇♥❳➽❩❇❳P❱q❩❇❵ ❳✽s❦❬✍③✕❬✍♦✽♦✽♠❛❩❭❬✍♦P❝✝❵ ❞✖❞q❵❨❦❚♥❵➦❉♦❫❬✍❚❲❱❇③❝✝❵❚♥♦P❩❇❳❫❬✍❞q❳❫❪✝➝❲❝➧♠♥❞q❵❚♥❨✮❱❇✇♥❳ ➺✸⑦✼❸❾➝❉❬✍❞q❳❫❪✧③ ❳❫❬✣❩❇❚♥❵❚♥❨❣❢❣❳✽♦❭✇❉❬✍❚♥❵ ❞q❢✵➟ ➾✿➚♥➪❁➶✧➹▲➘❲➴✕➷❛➬❲■✣❅q➮✣➱❛✃✵✃✵■✍◆✽➮✣■❛❐❫◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋➽◗❭▼⑧◗❇❒P■✍✃①◗✍❐❫❮➊❖❊❰✍❰✍▼✡Ï➱❛Ð❛❃④➮✦❐✍◗❭➱✦❮➀❒✈Ñ♥❖❊■✍◆P▼❛❐ ➮✣Ï❖▲◗❇❒P■✍◆P❃❋❊Ð▲❐⑧Ð❛■✍❋❊■✣❒P❃④➮➽❏✦ÏÐ❛➱❛◆P❃❒P❈❊✃✝❐⑧◆P■✍Ï■✍Ò❲❏✦❋▲➮✣■❄❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó Ô Õ♥Ö❍×⑧Ø❊Ù✱Ú✡Û✡Ü➩×❊Ý❇Ù❍Ö Þ◗✛❒P❈❊■☞❏✦✃✵➱❛❖❊❋♥❒✧➱✦❮✡❏tÒ❲❏✦❃ Ï④❏✦❘❊Ï■①ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗✧❃❋à■✣❅q➮✣➱❛✃✵✃✵■✍◆✽➮✣■➢❘❊❖▲◗❭❃❋❊■❦◗P◗❭■❦◗✛❃④◗✛❘❊❖❊◆PÐ❛■✍➱❛❋⑧❅ ❃❋❊Ð▲❐✼◗❭■❦❏✦◆✽➮✽❈❊❃❋❊Ð✢❮➊➱❛◆①❑⑧■❦◗❭❃ ◆P■❦❑➯ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗①❏✦✃✵➱❛❋❊Ðá■✍❋❊➱❛◆P✃✵➱❛❖▲◗✮➱✦â➤■✍◆P❃❋❊Ð♥◗✮❃④◗✵❘➩■❦➮✣➱❛✃✵❃❋❊Ð ❃❋▲➮✣◆P■❦❏❛◗❭❃❋❊Ð❛Ï▼✧❑⑧❃ã➢➮✣❖❊Ï❒❦ä Þ◗✐❏➽◆P■❦◗❭❖❊Ï❒❦❐❛■✣❅q➮✣➱❛✃✵✃✵■✍◆✽➮✣■✱❖▲◗❭■✍◆✽◗②❮➊◆P■❦Ñ♥❖❊■✍❋♥❒PÏ▼✮◗❭❖⑧â➤■✍◆✼❮➊◆P➱❛✃➫❃❋⑧❅ ❮➊➱❛◆P✃①❏❲❒P❃➱❛❋å➱❲Ò❛■✍◆PÏ➱♥❏❛❑❁ä▲æ✈➱☞❏✦Ï Ï■✍Ò❉❃④❏❲❒P■✛❒P❈❊❃④◗✡ß❊◆P➱❛❘❊Ï■✍✃✝❐➩◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋✢◗❭▼⑧◗❇❒P■✍✃①◗❄❏✦◆P■ ❘➩■✍❃❋❊Ð✝ç✱❃④❑⑧■✍Ï▼✢❏❛❑⑧➱❛ß⑧❒P■❦❑å❒P➱✝❈❊■✍Ïßà➮✣❖▲◗❇❒P➱❛✃✵■✍◆✽◗❄Ï➱⑧➮✍❏❲❒P■✮ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗❄❒P❈❊■✍▼✢ç✐➱❛❖❊Ï④❑åÏ ❃Ó❛■✮❒P➱ ß❊❖❊◆✽➮✽❈▲❏❛◗❭■❛ä❲èq❋①■❦◗P◗❭■✍❋▲➮✣■❛❐t❒P❈❊■❦◗❭■✖◗❭▼⑧◗❇❒P■✍✃①◗✼❏✦ß❊ß❊Ï▼✮❑❊❏❲❒✽❏❣❏✦❋▲❏✦Ï▼⑧◗❭❃④◗✕❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■❦◗②❒P➱❣ß❊◆P➱❲Ò❉❃④❑⑧■ ❏①Ï ❃④◗❇❒❄➱✦❮✼◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑⑧■❦❑✝ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗✱❮➊➱❛◆❄■❦❏❛➮✽❈✿➱❛❋❊Ï ❃❋❊■✧➮✣❖▲◗❇❒P➱❛✃✵■✍◆❦ä➩æ✸❈❊■✧✃✵➱♥◗❇❒✖❮❾❏✦✃✵➱❛❖▲◗ ■✣é❊❏✦✃✵ß❊Ï■✿❃❋✫■✣❅q➮✣➱❛✃✵✃✵■✍◆✽➮✣■✝❘❊❖▲◗❭❃❋❊■❦◗P◗❭■❦◗✵❃④◗①❒P❈❊■✫ê❛ë②ì❉í❫îqï❲ð①ñ✣ò✽í✝ó✕ô❊ïöõ✽ï❲ì❛÷tô❉î④øá❮➊■❦❏❲❒P❖❊◆P■ ❖▲◗❭■❦❑☞❃❋ Þ✃①❏✦❰✍➱❛❋✂ä ➮✣➱❛✃①ùtú❍❐❊ç✱❈❊❃④➮✽❈☞❃④◗✱❘▲❏❛◗❭❃④➮✍❏✦Ï Ï▼➢❏✦ß❊ß❊Ï ❃■❦❑➧❒P➱✵■✍Ò❛■✍◆P▼➢ß❊◆P➱⑧❑⑧❖▲➮❫❒✱ß▲❏✦Ð❛■➽➱❛❋ ❃❒✽◗➽ç✐■✍❘▲◗❭❃❒P■❦◗✍ä➤û✫❃❒P❈✢❒P❈❊■✵❈❊■✍Ïß✢➱✦❮❍❒P❈❊❃④◗✡❮➊■❦❏❲❒P❖❊◆P■❛❐ Þ✃①❏✦❰✍➱❛❋✂ä ➮✣➱❛✃①ùtú✐ü ◗➽◗❭▼⑧◗❇❒P■✍✃✘◆P■❦➮✣➱❛✃✮❅ ✃✵■✍❋▲❑❊◗✱◗❭❃✃✵❃ Ï④❏✦◆✸ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗❍❒P➱✧❒P❈❊■❣➮✣❖❊◆P◆P■✍❋♥❒✸❘❊❖❊▼❛■✍◆✸❘▲❏❛◗❭■❦❑➢➱❛❋➧❒P❈❊■➽ß❊❖❊◆✽➮✽❈▲❏❛◗❭■✡❈❊❃④◗❇❒P➱❛◆P❃■❦◗ ➱✦❮②ß❊◆P■✍Ò❉❃➱❛❖▲◗✸➮✣❖▲◗❇❒P➱❛✃✵■✍◆✽◗✸ç✱❈❊➱✵❘➩➱❛❖❊Ð❛❈♥❒✸❒P❈❊■✛◗P❏✦✃✵■➽ß❊◆P➱⑧❑⑧❖▲➮❫❒❦ä ý ➠②✇♥❵ ❞✂❩❇❳✽❞q❳❫❬✣❩❇♦❭✇❣✇❉❬✍❞✂➝⑧❳✽❳✽❚❄✉④♠♥❚❉❪❛❳❫❪✡❵❚❣❧❉❬✣❩q❱✈➝❲❝✡þ✱❜❛ÿ✵❨✍❩❭❬✍❚❲❱❇❞✕❽✈❽✕❤✈❸❯⑨t❺❦❹✍⑨✁￾✣❺❦❹✛➸➀❙q❴①❜♥❤✿❽➛❤✟➻ ❬✍❚❉❪å❙❡➠➛❸❯❶❦❶❦❷t❹✍❷t❹✍❻✦⑤②þ✐❙❯➨❍❸❾þ✸⑥➤❴ ➛❶✁￾P❸❯⑥➤❴✵❶✄✂✍❶❦❻✁￾❦⑤②♣✐⑦➛✆☎⑦ ❬✍❚❉❪år✱❜❛⑦✐ÿö♠♥❚❉❪❛❳P❩❣❬✍❨✍❩❇❳✽❳P❸ ❢❣❳✽❚❲❱❄❚❛❩❫➟✂ÿ✞✝❦❶❦❻❦❶t❹❫❸❯⑨❦⑨✣❸✟￾P❸❯❶t❺❦❹✣❼❛⑤✟❬✍❚❉❪✿♠♥❚❛❩❇❳✽❞❡❱q❩❇❵ ♦P❱❇❳❫❪✿♦❫❬✍❞q✇✡✠✣❳☞☛❲♠♥❵❧♥❢❣❳✽❚❲❱❄❨❦❵✉❱❇❞➽✉❩❇❥❦❢✥þ✱❤➛⑤ ❙✍✌✟❴➧⑤♥❙❯❚❲❱❇❳✽③▲❬✍❚❉❪①❜✦r✐þ✡➟

Yi-Shin Chen and Cyrus Shahabi Systems such as Amazon comM employ filtering techniques which fall into two classes: content-based filtering and collaborative filtering. Both types of systems have inherent strengths and weaknesses, where content-based ap- proaches directly exploit the product information, and the collaboration fil- tering approaches utilize specific user rating information. The content-based filtering approach generates recommendation lists based comparisons between the feature vectors of products(e.g artist, style)in he database with those in the user's profile Hence, the accuracy of the users profile is very important. To keep the user profile accurate, various learning techniques, such as Bayesian clas sifiers, neural networks, and genetic alge profiles[14-16 Despite these improvements, this approach has several other weaknesses One is content limitation, i. e, lexical fragment methods can only be applied o text content. The other is over-specialization, i.e., users can only obtain new information they might desire. Moreover, because of the complexity of user profiles, the learning processes are always computationally costly and unable to adapt to frequent ly changing user preferences On the other hand, the collaborative filtering(CF)approach, does not use any information regarding the act ual content of the products. The approach is based on the assumption that people having similar interests will possibl like the same objects. Typically, CF-based recommendation systems utilize users'rating of products to generate recommendation lists. Therefore, the over-specialization problem is avoided since a user could explore new items sted in other users' profiles. The nearest-neighbor al gorithm is the earliest CF-based technique used in recommendation systems [12. With this algorithm, the similarity between users is evaluated based on their ratings of products, and the recommendation is generated considering the items visited by nearest neighbors of the user In its original form, CF-based recommendations suffer from the problems of scalability, sparsity, and synonymy (i.e, latent association between items is not considered for recommendations .) In order to alleviat eliminate researchers introduced a variety of different techniques into collaborative tering systems, such as content analysis 11 for avoiding the synonymy and sparsity problems; categorization [13 to alleviate the synonymy and span sity problems; Bayesian network 9, 8 for lightening the scalability problems clustering 9 to lessen sparsity and scalability problems; and Singular Value Decomposition(SVD)[10, 7 to ease all three problems. However, all these techniques have limitation and do not work well in all general case In an earlier work [1], we introduced a hybrid recommendation system Yoda, which simultaneously utilizes the advantages of clustering, content analysis, and collaborate filtering(CF)approaches. Bas Yoda step approach recommendation sy stem. Basically, during the offline process

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◆P■❛ä❀✿✿➱❛◆P■✍➱❲Ò❛■✍◆❦❐▲❘➩■❦➮✍❏✦❖▲◗❭■✮➱✦❮✼❒P❈❊■①➮✣➱❛✃✵ß❊Ï■✣é⑧❃❒❇▼✿➱✦❮ ❖▲◗❭■✍◆✧ß❊◆P➱✥￾▲Ï■❦◗✍❐✂❒P❈❊■☞Ï■❦❏✦◆P❋❊❃❋❊Ð✿ß❊◆P➱⑧➮✣■❦◗P◗❭■❦◗✧❏✦◆P■➧❏✦Ïç✸❏t▼⑧◗✛➮✣➱❛✃✵ß❊❖⑧❒✽❏❲❒P❃➱❛❋▲❏✦Ï Ï▼ ➮✣➱♥◗❇❒PÏ▼ ❏✦❋▲❑ ❖❊❋▲❏✦❘❊Ï■➽❒P➱①❏❛❑❊❏✦ß⑧❒✸❒P➱✵❮➊◆P■❦Ñ♥❖❊■✍❋♥❒PÏ▼➧➮✽❈▲❏✦❋❊Ð❛❃❋❊Ð✮❖▲◗❭■✍◆✱ß❊◆P■✣❮➊■✍◆P■✍❋▲➮✣■❦◗✍ä ✺❋❣❒P❈❊■✸➱✦❒P❈❊■✍◆②❈▲❏✦❋▲❑❁❐❦❒P❈❊■✸➮✣➱❛Ï Ï④❏✦❘➩➱❛◆✽❏❲❒P❃Ò❛■❁￾▲Ï❒P■✍◆P❃❋❊Ð❂✜❡●❄❃❁✣✕❏✦ß❊ß❊◆P➱♥❏❛➮✽❈✂❐❦❑⑧➱❉■❦◗✕❋❊➱✦❒②❖▲◗❭■ ❏✦❋❉▼①❃❋⑧❮➊➱❛◆P✃①❏❲❒P❃➱❛❋☞◆P■✍Ð♥❏✦◆✽❑⑧❃❋❊Ð✛❒P❈❊■❣❏❛➮❫❒P❖▲❏✦Ï✂➮✣➱❛❋♥❒P■✍❋♥❒✸➱✦❮✈❒P❈❊■❣ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗✍ä❊æ✸❈❊■❣❏✦ß❊ß❊◆P➱♥❏❛➮✽❈ ❃④◗✸❘▲❏❛◗❭■❦❑➢➱❛❋➧❒P❈❊■❣❏❛◗P◗❭❖❊✃✵ß⑧❒P❃➱❛❋➢❒P❈▲❏❲❒✱ß➩■✍➱❛ß❊Ï■➽❈▲❏tÒ❉❃❋❊Ð✵◗❭❃✃✵❃ Ï④❏✦◆✸❃❋♥❒P■✍◆P■❦◗❇❒✽◗✐ç✱❃ Ï Ï❁ß➩➱♥◗P◗❭❃❘❊Ï▼ Ï ❃Ó❛■✮❒P❈❊■➢◗P❏✦✃✵■✵➱❛❘❆❅❇■❦➮❫❒✽◗✍ä✈æ❍▼❉ß❊❃④➮✍❏✦Ï Ï▼❛❐✕●❄❃✈❅❡❘▲❏❛◗❭■❦❑✢◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋á◗❭▼⑧◗❇❒P■✍✃①◗➽❖⑧❒P❃ Ï ❃❰✍■ ❖▲◗❭■✍◆✽◗✍ü✕◆✽❏❲❒P❃❋❊Ðá➱✦❮✡ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗✧❒P➱áÐ❛■✍❋❊■✍◆✽❏❲❒P■➧◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋ Ï ❃④◗❇❒✽◗✍ä✟æ✸❈❊■✍◆P■✣❮➊➱❛◆P■❛❐✕❒P❈❊■ ➱❲Ò❛■✍◆❭❅q◗❭ß➩■❦➮✣❃④❏✦Ï ❃❰❦❏❲❒P❃➱❛❋✿ß❊◆P➱❛❘❊Ï■✍✃✘❃④◗➽❏tÒ❛➱❛❃④❑⑧■❦❑å◗❭❃❋▲➮✣■✵❏➧❖▲◗❭■✍◆❣➮✣➱❛❖❊Ï④❑✢■✣é⑧ß❊Ï➱❛◆P■✧❋❊■✍ç✙❃❒P■✍✃①◗ Ï ❃④◗❇❒P■❦❑☞❃❋✝➱✦❒P❈❊■✍◆✱❖▲◗❭■✍◆✽◗✍ü⑧ß❊◆P➱✥￾▲Ï■❦◗✍ä æ✸❈❊■✵❋❊■❦❏✦◆P■❦◗❇❒❭❅❡❋❊■✍❃Ð❛❈❉❘➩➱❛◆❣❏✦ÏÐ❛➱❛◆P❃❒P❈❊✃✘❃④◗➽❒P❈❊■①■❦❏✦◆PÏ ❃■❦◗❇❒✧●❄❃✈❅❡❘▲❏❛◗❭■❦❑å❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■①❖▲◗❭■❦❑ ❃❋①◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋①◗❭▼⑧◗❇❒P■✍✃①◗✢✯ ✱✤✷✤✸❡ä✦û✫❃❒P❈①❒P❈❊❃④◗❍❏✦ÏÐ❛➱❛◆P❃❒P❈❊✃✝❐❲❒P❈❊■❄◗❭❃✃✵❃ Ï④❏✦◆P❃❒❇▼✧❘➩■✣❒❇ç✐■✍■✍❋ ❖▲◗❭■✍◆✽◗✂❃④◗✂■✍Ò❲❏✦Ï❖▲❏❲❒P■❦❑➽❘▲❏❛◗❭■❦❑❣➱❛❋➽❒P❈❊■✍❃ ◆✈◆✽❏❲❒P❃❋❊Ð♥◗✂➱✦❮⑧ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗✍❐t❏✦❋▲❑❄❒P❈❊■❍◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋ ❃④◗➽Ð❛■✍❋❊■✍◆✽❏❲❒P■❦❑✢➮✣➱❛❋▲◗❭❃④❑⑧■✍◆P❃❋❊Ð➧❒P❈❊■①❃❒P■✍✃①◗❣Ò❉❃④◗❭❃❒P■❦❑✢❘❉▼å❋❊■❦❏✦◆P■❦◗❇❒➽❋❊■✍❃Ð❛❈❉❘➩➱❛◆✽◗❄➱✦❮✐❒P❈❊■✵❖▲◗❭■✍◆❦ä èq❋➧❃❒✽◗✐➱❛◆P❃Ð❛❃❋▲❏✦Ï❊❮➊➱❛◆P✃✝❐▲●❄❃✈❅❡❘▲❏❛◗❭■❦❑①◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋▲◗❍◗❭❖⑧â➤■✍◆✐❮➊◆P➱❛✃✭❒P❈❊■❄ß❊◆P➱❛❘❊Ï■✍✃①◗❍➱✦❮❇❐ ➷✶❇❉❈❋❊✪❈❆●■❍✪❊✪❍❑❏✽➪✟❐❲➷✄▲■❈⑧➘❲➷✶❍❑❏✽➪✟❐✦❏✦❋▲❑✧➷✍➪◆▼✕➹✆▼➤➪◆❖✢➪P✜➊❃❯ä ■❛ä ❐❲Ï④❏❲❒P■✍❋♥❒✟❏❛◗P◗❭➱⑧➮✣❃④❏❲❒P❃➱❛❋❣❘➩■✣❒❇ç✐■✍■✍❋✮❃❒P■✍✃①◗ ❃④◗✱❋❊➱✦❒✖➮✣➱❛❋▲◗❭❃④❑⑧■✍◆P■❦❑➢❮➊➱❛◆✱◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋▲◗✍ä ✣ èq❋ ➱❛◆✽❑⑧■✍◆➧❒P➱ö❏✦Ï Ï■✍Ò❉❃④❏❲❒P■á➱❛◆☞■✍Ò❛■✍❋ ■✍Ï ❃✃✵❃❋▲❏❲❒P■á❒P❈❊■❦◗❭■áß❊◆P➱❛❘❊Ï■✍✃①◗✍❐✐✃✵➱❛◆P■✢◆P■❦➮✣■✍❋♥❒PÏ▼❛❐ ◆P■❦◗❭■❦❏✦◆✽➮✽❈❊■✍◆✽◗✼❃❋♥❒P◆P➱⑧❑⑧❖▲➮✣■❦❑☞❏✮Ò❲❏✦◆P❃■✣❒❇▼①➱✦❮②❑⑧❃â➤■✍◆P■✍❋♥❒✸❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■❦◗✸❃❋♥❒P➱①➮✣➱❛Ï Ï④❏✦❘➩➱❛◆✽❏❲❒P❃Ò❛■✛￾▲Ï ❅ ❒P■✍◆P❃❋❊Ð➧◗❭▼⑧◗❇❒P■✍✃①◗✍❐▲◗❭❖▲➮✽❈å❏❛◗★☎✽ï✞✝▲îqñ✟✝▲î◗☛✞✝✴☛✞✏✩tí✓✒④í★✯ ✱❉✱✟✸✂❮➊➱❛◆❄❏tÒ❛➱❛❃④❑⑧❃❋❊Ð✮❒P❈❊■✧◗❭▼❉❋❊➱❛❋❉▼❉✃✧▼☞❏✦❋▲❑ ◗❭ß▲❏✦◆✽◗❭❃❒❇▼àß❊◆P➱❛❘❊Ï■✍✃①◗✄❘✛☎☞☛❲îqñ❡÷♥ï❲ò✓✒✾✶☛❲î✡✒❾ï✞✝❙✯ ✱✶❚✞✸✸❒P➱ ❏✦Ï Ï■✍Ò❉❃④❏❲❒P■➧❒P❈❊■✿◗❭▼❉❋❊➱❛❋❉▼❉✃✧▼ ❏✦❋▲❑ö◗❭ß▲❏✦◆❭❅ ◗❭❃❒❇▼✵ß❊◆P➱❛❘❊Ï■✍✃①◗✄❘❋✧✢☛✞✩❛ñ❫í✓✒✪☛✞✝✕✝➤ñ✣î❯ó❍ï❲ò✖✬★✯❯❊❐ ❱✞✸➩❮➊➱❛◆✐Ï ❃Ð❛❈♥❒P■✍❋❊❃❋❊Ð✛❒P❈❊■❄◗P➮✍❏✦Ï④❏✦❘❊❃ Ï ❃❒❇▼✵ß❊◆P➱❛❘❊Ï■✍✃①◗✄❘ ☎✟✏ì❉í❫îqñ✣ò✓✒✔✝❉÷❲✯❯✞✸❁❒P➱✵Ï■❦◗P◗❭■✍❋✝◗❭ß▲❏✦◆✽◗❭❃❒❇▼➢❏✦❋▲❑✝◗P➮✍❏✦Ï④❏✦❘❊❃ Ï ❃❒❇▼①ß❊◆P➱❛❘❊Ï■✍✃①◗✄❘❊❏✦❋▲❑✝❆❉❃❋❊Ð❛❖❊Ï④❏✦◆◗❳✼❏✦Ï❖❊■ ✹✡■❦➮✣➱❛✃✵ß➩➱♥◗❭❃❒P❃➱❛❋❨✜❯❆✙❳❩✹✭✣❬✯ ✱✶❭❊❐ ❪✶✸②❒P➱☞■❦❏❛◗❭■✵❏✦Ï Ï✕❒P❈❊◆P■✍■✵ß❊◆P➱❛❘❊Ï■✍✃①◗✍ä❀✦✖➱❲ç✐■✍Ò❛■✍◆❦❐➤❏✦Ï Ï✕❒P❈❊■❦◗❭■ ❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■❦◗✸❈▲❏tÒ❛■➽Ï ❃✃✵❃❒✽❏❲❒P❃➱❛❋✿❏✦❋▲❑☞❑⑧➱✵❋❊➱✦❒✖ç✐➱❛◆PÓ①ç✐■✍Ï Ï❁❃❋✿❏✦Ï Ï✂Ð❛■✍❋❊■✍◆✽❏✦Ï❁➮✍❏❛◗❭■❦◗✍ä èq❋➯❏✦❋➯■❦❏✦◆PÏ 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Improving User Profiles for E-Commerce by Genetic Algorithms Yoda maintains numerous recommendation lists obt ained from human ex perts, clusters of user evaluations, and web navigation patterns analy zed by lustering and content analysis techniques. During the online process, the onfidence values of an active user to the experts are estimated and kept in the user profile. By utilizing the user profile and experts'recommenda tions, Yoda finally generates customized recommendations for the user. As a result, we reduce the time complexity through model based and clustering ap- proaches, alleviate the synonymy problem with content analysis, and address he sparsity problem by implicit identification of the users interests 5, 6 Since Yoda relies on user profiles to recommend products, the accuracy of recommendation results will decline if the user profiles are inaccurate. In practice, obtaining user profiles by explicit acquisitions has been challenging. We utilized the users'relevance feedback and improved the profiles automat ically using GA [4]. To the best of our knowledge, only a few studies [3, 2] rate ga fo the user profiles. In the directly involved in the evolution processes. Because users have to enter data for each product inquiry, they are often frustrated with this method. On the contrary . in design, users are not required to offer additional dat to improve the confidence values. These confidence values are corrected by the gA-based learning mechanisms using users' future navigation behaviors That is, Yoda assumes positive feedback from a user when the user actually navigates through Yoda's recommended items. Our experimental results in dicate a to the integration of the proposed learning mechanism The remainder of this paper is organized as follows. Section 2 describes the concept of genetic algorithms. In Section 3, we provide an overview the functionality of Yoda. In Section 4, we discuss the detailed design of Yoda and the learning mechanism. The results of our evaluations as well as he details of the sy stem implementation and our benchmarking method are described in Section 5. Section 6 concludes the paper 2 Genetic algorithms Genetic algorithms(GAs), which were introduced by Holland [4, are itera- tive search techniques based on the spirit of natural evolution. By emulating biological selection and reproduction, GAs can efficiently search through the solution space of complex problems. Basically, a Ga operates on a popu- lation of candidate solutions called chromosomes. A chromosome which composed of numerous genes, represents an encoding of the problem and as sociates it with a fitness value evaluated by the fitness function This fitness value determines the goodness and the survival ability of the chromosome Generally, Ga starts by initializing the population and evaluating its cor- responding fitness values. Before it terminates, Ga produces newer genera- tions iteratively At each generation, a portion of the chromosomes is selected

❙❯❢❣❧❛❩❇❥✣s✦❵❚♥❨✡r✐❞q❳P❩ ☎❩❇❥✍➦❉③ ❳✽❞✟✉④❥✍❩✸❽❁❸❡❤✕❥❦❢❣❢❣❳P❩❇♦✽❳✸➝❲❝✮➺✐❳✽❚♥❳P❱❇❵ ♦✖⑦❍③❨❦❥✍❩❇❵❱❇✇♥❢❣❞ ✝ ❂✼➱⑧❑❊❏á✃①❏✦❃❋♥❒✽❏✦❃❋▲◗✮❋❉❖❊✃✵■✍◆P➱❛❖▲◗✧◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋➯Ï ❃④◗❇❒✽◗✮➱❛❘⑧❒✽❏✦❃❋❊■❦❑ ❮➊◆P➱❛✃ ❈❉❖❊✃①❏✦❋➯■✣é❉❅ ß➩■✍◆❭❒✽◗✍❐➩➮✣Ï❖▲◗❇❒P■✍◆✽◗✸➱✦❮✟❖▲◗❭■✍◆✖■✍Ò❲❏✦Ï❖▲❏❲❒P❃➱❛❋▲◗✍❐❊❏✦❋▲❑✝ç✐■✍❘✝❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋☞ß▲❏❲❒❭❒P■✍◆P❋▲◗✖❏✦❋▲❏✦Ï▼❉❰✍■❦❑➧❘❉▼ ➮✣Ï❖▲◗❇❒P■✍◆P❃❋❊Ðà❏✦❋▲❑⑩➮✣➱❛❋♥❒P■✍❋♥❒➢❏✦❋▲❏✦Ï▼⑧◗❭❃④◗✧❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■❦◗✍ä✎✹✡❖❊◆P❃❋❊Ðá❒P❈❊■✿➱❛❋❊Ï ❃❋❊■✿ß❊◆P➱⑧➮✣■❦◗P◗✍❐②❒P❈❊■ ➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■✝Ò❲❏✦Ï❖❊■❦◗✵➱✦❮❣❏✦❋⑩❏❛➮❫❒P❃Ò❛■✝❖▲◗❭■✍◆✮❒P➱ ❒P❈❊■✿■✣é⑧ß➩■✍◆❭❒✽◗①❏✦◆P■☞■❦◗❇❒P❃✃①❏❲❒P■❦❑⑩❏✦❋▲❑➯Ó❛■✍ß⑧❒ ❃❋ ❒P❈❊■➧❖▲◗❭■✍◆✮ß❊◆P➱✥￾▲Ï■❛ä■✘✐▼ ❖⑧❒P❃ Ï ❃❰✍❃❋❊Ðå❒P❈❊■☞❖▲◗❭■✍◆✮ß❊◆P➱✥￾▲Ï■➧❏✦❋▲❑à■✣é⑧ß➩■✍◆❭❒✽◗✍ü✕◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❅ ❒P❃➱❛❋▲◗✍❐♥❂✼➱⑧❑❊❏ ￾▲❋▲❏✦Ï Ï▼✵Ð❛■✍❋❊■✍◆✽❏❲❒P■❦◗✐➮✣❖▲◗❇❒P➱❛✃✵❃❰✍■❦❑➢◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋▲◗✟❮➊➱❛◆✐❒P❈❊■❄❖▲◗❭■✍◆❦ä Þ◗✸❏ ◆P■❦◗❭❖❊Ï❒❦❐❦ç✐■✐◆P■❦❑⑧❖▲➮✣■✼❒P❈❊■❍❒P❃✃✵■✸➮✣➱❛✃✵ß❊Ï■✣é⑧❃❒❇▼❄❒P❈❊◆P➱❛❖❊Ð❛❈❣✃✵➱⑧❑⑧■✍Ï❉❘▲❏❛◗❭■❦❑✛❏✦❋▲❑✧➮✣Ï❖▲◗❇❒P■✍◆P❃❋❊Ð❄❏✦ß⑧❅ ß❊◆P➱♥❏❛➮✽❈❊■❦◗✍❐✦❏✦Ï Ï■✍Ò❉❃④❏❲❒P■✸❒P❈❊■✡◗❭▼❉❋❊➱❛❋❉▼❉✃✧▼✛ß❊◆P➱❛❘❊Ï■✍✃➫ç✱❃❒P❈➢➮✣➱❛❋♥❒P■✍❋♥❒❍❏✦❋▲❏✦Ï▼⑧◗❭❃④◗✍❐❛❏✦❋▲❑✵❏❛❑❊❑⑧◆P■❦◗P◗ ❒P❈❊■✛◗❭ß▲❏✦◆✽◗❭❃❒❇▼①ß❊◆P➱❛❘❊Ï■✍✃ ❘❉▼➧❃✃✵ß❊Ï ❃④➮✣❃❒✖❃④❑⑧■✍❋♥❒P❃￾➩➮✍❏❲❒P❃➱❛❋☞➱✦❮✕❒P❈❊■❣❖▲◗❭■✍◆✽◗✸❃❋♥❒P■✍◆P■❦◗❇❒✽◗✰✯ ✁ ❐ ✵✞✸❡ä ❆❉❃❋▲➮✣■✮❂✼➱⑧❑❊❏☞◆P■✍Ï ❃■❦◗➽➱❛❋✢❖▲◗❭■✍◆❣ß❊◆P➱✥￾▲Ï■❦◗✡❒P➱✝◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑✢ß❊◆P➱⑧❑⑧❖▲➮❫❒✽◗✍❐➤❒P❈❊■➢❏❛➮✍➮✣❖❊◆✽❏❛➮✣▼ ➱✦❮✼◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋✝◆P■❦◗❭❖❊Ï❒✽◗✖ç✱❃ Ï Ï②❑⑧■❦➮✣Ï ❃❋❊■✧❃❮✟❒P❈❊■✮❖▲◗❭■✍◆✡ß❊◆P➱✥￾▲Ï■❦◗✖❏✦◆P■✛❃❋▲❏❛➮✍➮✣❖❊◆✽❏❲❒P■❛ä▲èq❋ ß❊◆✽❏❛➮❫❒P❃④➮✣■❛❐✦➱❛❘⑧❒✽❏✦❃❋❊❃❋❊Ð❣❖▲◗❭■✍◆✼ß❊◆P➱✥￾▲Ï■❦◗✟❘❉▼✮■✣é⑧ß❊Ï ❃④➮✣❃❒✐❏❛➮✍Ñ♥❖❊❃④◗❭❃❒P❃➱❛❋▲◗✟❈▲❏❛◗✟❘➩■✍■✍❋➢➮✽❈▲❏✦Ï Ï■✍❋❊Ð❛❃❋❊Ð▲ä ûá■✡❖⑧❒P❃ Ï ❃❰✍■❦❑✵❒P❈❊■✡❖▲◗❭■✍◆✽◗✍ü✦◆P■✍Ï■✍Ò❲❏✦❋▲➮✣■✱❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó✵❏✦❋▲❑✵❃✃✵ß❊◆P➱❲Ò❛■❦❑✧❒P❈❊■✡ß❊◆P➱✥￾▲Ï■❦◗❍❏✦❖⑧❒P➱❛✃①❏❲❒❭❅ ❃④➮✍❏✦Ï Ï▼á❖▲◗❭❃❋❊ÐP✮Þ ✯✲✥✸❡ä✕æ✈➱✿❒P❈❊■➧❘➩■❦◗❇❒✮➱✦❮✱➱❛❖❊◆✛Ó❉❋❊➱❲ç✱Ï■❦❑⑧Ð❛■❛❐✂➱❛❋❊Ï▼ ❏✝❮➊■✍ç ◗❇❒P❖▲❑⑧❃■❦◗✚✯❚❊❐ ✷✤✸ ❃❋▲➮✣➱❛◆Pß➩➱❛◆✽❏❲❒P■✚✮Þ ❮➊➱❛◆✮❃✃✵ß❊◆P➱❲Ò❉❃❋❊Ð✿❒P❈❊■➧❖▲◗❭■✍◆✮ß❊◆P➱✥￾▲Ï■❦◗✍ä✈èq❋à❒P❈❊■❦◗❭■☞◗❇❒P❖▲❑⑧❃■❦◗✍❐②❖▲◗❭■✍◆✽◗✧❏✦◆P■ ❑⑧❃ ◆P■❦➮❫❒PÏ▼✮❃❋❉Ò❛➱❛ÏÒ❛■❦❑✮❃❋①❒P❈❊■✖■✍Ò❛➱❛Ï❖⑧❒P❃➱❛❋①ß❊◆P➱⑧➮✣■❦◗P◗❭■❦◗✍ä❉✘✐■❦➮✍❏✦❖▲◗❭■✱❖▲◗❭■✍◆✽◗✟❈▲❏tÒ❛■✸❒P➱✧■✍❋♥❒P■✍◆❍❑❊❏❲❒✽❏ ❮➊➱❛◆➧■❦❏❛➮✽❈⑩ß❊◆P➱⑧❑⑧❖▲➮❫❒➧❃❋▲Ñ♥❖❊❃ ◆P▼❛❐✼❒P❈❊■✍▼✫❏✦◆P■✿➱✦❮➀❒P■✍❋⑩❮➊◆P❖▲◗❇❒P◆✽❏❲❒P■❦❑⑩ç✱❃❒P❈✫❒P❈❊❃④◗➢✃✵■✣❒P❈❊➱⑧❑❁ä ✺❋ ❒P❈❊■☞➮✣➱❛❋♥❒P◆✽❏✦◆P▼❛❐✂❃❋ ➱❛❖❊◆✵❑⑧■❦◗❭❃Ð❛❋✂❐✈❖▲◗❭■✍◆✽◗✧❏✦◆P■➢❋❊➱✦❒✮◆P■❦Ñ♥❖❊❃ ◆P■❦❑á❒P➱✢➱✦â➤■✍◆✮❏❛❑❊❑⑧❃❒P❃➱❛❋▲❏✦Ï✐❑❊❏❲❒✽❏ ❒P➱✿❃✃✵ß❊◆P➱❲Ò❛■✮❒P❈❊■➧➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■✵Ò❲❏✦Ï❖❊■❦◗✍ä✈æ✸❈❊■❦◗❭■➢➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■①Ò❲❏✦Ï❖❊■❦◗✛❏✦◆P■①➮✣➱❛◆P◆P■❦➮❫❒P■❦❑✢❘❉▼ ❒P❈❊■✰✮Þ❅❡❘▲❏❛◗❭■❦❑➢Ï■❦❏✦◆P❋❊❃❋❊Ð✧✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃①◗❍❖▲◗❭❃❋❊Ð✮❖▲◗❭■✍◆✽◗✍ü❛❮➊❖⑧❒P❖❊◆P■❄❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋①❘➩■✍❈▲❏tÒ❉❃➱❛◆✽◗✍ä æ✸❈▲❏❲❒✖❃④◗✍❐⑧❂✼➱⑧❑❊❏①❏❛◗P◗❭❖❊✃✵■❦◗✸ß➩➱♥◗❭❃❒P❃Ò❛■❄❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó①❮➊◆P➱❛✃✥❏✵❖▲◗❭■✍◆✱ç✱❈❊■✍❋☞❒P❈❊■❣❖▲◗❭■✍◆✡❏❛➮❫❒P❖▲❏✦Ï Ï▼ ❋▲❏tÒ❉❃Ð♥❏❲❒P■❦◗✸❒P❈❊◆P➱❛❖❊Ð❛❈✝❂✼➱⑧❑❊❏❊ü ◗✖◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑⑧■❦❑✝❃❒P■✍✃①◗✍ä ✺❖❊◆✡■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒✽❏✦Ï✈◆P■❦◗❭❖❊Ï❒✽◗✖❃❋⑧❅ ❑⑧❃④➮✍❏❲❒P■①❏☞◗❭❃Ð❛❋❊❃￾➩➮✍❏✦❋♥❒➽❃❋▲➮✣◆P■❦❏❛◗❭■✮❃❋✢❒P❈❊■①❏❛➮✍➮✣❖❊◆✽❏❛➮✣▼✝➱✦❮✐◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋å◆P■❦◗❭❖❊Ï❒✽◗❣❑⑧❖❊■ ❒P➱✵❒P❈❊■➽❃❋♥❒P■✍Ð❛◆✽❏❲❒P❃➱❛❋☞➱✦❮✈❒P❈❊■❣ß❊◆P➱❛ß➩➱♥◗❭■❦❑➧Ï■❦❏✦◆P❋❊❃❋❊Ð✮✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃✝ä æ✸❈❊■✮◆P■✍✃①❏✦❃❋▲❑⑧■✍◆➽➱✦❮❍❒P❈❊❃④◗➽ß▲❏✦ß➩■✍◆➽❃④◗➽➱❛◆PÐ♥❏✦❋❊❃❰✍■❦❑å❏❛◗✡❮➊➱❛Ï Ï➱❲ç✖◗✍ä❁❆❉■❦➮❫❒P❃➱❛❋ ✷➧❑⑧■❦◗P➮✣◆P❃❘➩■❦◗ ❒P❈❊■➢➮✣➱❛❋▲➮✣■✍ß⑧❒❣➱✦❮✐Ð❛■✍❋❊■✣❒P❃④➮①❏✦ÏÐ❛➱❛◆P❃❒P❈❊✃①◗✍ä➤èq❋à❆❉■❦➮❫❒P❃➱❛❋❵❚❊❐✂ç✐■✵ß❊◆P➱❲Ò❉❃④❑⑧■✵❏✦❋á➱❲Ò❛■✍◆PÒ❉❃■✍ç ➱❛❋ ❒P❈❊■✿❮➊❖❊❋▲➮❫❒P❃➱❛❋▲❏✦Ï ❃❒❇▼⑩➱✦❮➽❂✼➱⑧❑❊❏❊ä❍èq❋ ❆❉■❦➮❫❒P❃➱❛❋ ✲▲❐❍ç✐■å❑⑧❃④◗P➮✣❖▲◗P◗①❒P❈❊■✢❑⑧■✣❒✽❏✦❃ Ï■❦❑✫❑⑧■❦◗❭❃Ð❛❋✫➱✦❮ ❂✼➱⑧❑❊❏➢❏✦❋▲❑✝❒P❈❊■✧Ï■❦❏✦◆P❋❊❃❋❊Ð①✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃✝ä➩æ✸❈❊■✧◆P■❦◗❭❖❊Ï❒✽◗✖➱✦❮✟➱❛❖❊◆✡■✍Ò❲❏✦Ï❖▲❏❲❒P❃➱❛❋▲◗✖❏❛◗✖ç✐■✍Ï Ï②❏❛◗ ❒P❈❊■❣❑⑧■✣❒✽❏✦❃ Ï④◗✸➱✦❮✈❒P❈❊■❣◗❭▼⑧◗❇❒P■✍✃ ❃✃✵ß❊Ï■✍✃✵■✍❋♥❒✽❏❲❒P❃➱❛❋✝❏✦❋▲❑➧➱❛❖❊◆✸❘➩■✍❋▲➮✽❈❊✃①❏✦◆PÓ❉❃❋❊Ð✮✃✵■✣❒P❈❊➱⑧❑☞❏✦◆P■ ❑⑧■❦◗P➮✣◆P❃❘➩■❦❑➧❃❋å❆❉■❦➮❫❒P❃➱❛❋ ✁ ä▲❆❉■❦➮❫❒P❃➱❛❋ ✵✵➮✣➱❛❋▲➮✣Ï❖▲❑⑧■❦◗✐❒P❈❊■❣ß▲❏✦ß➩■✍◆❦ä ✂ ✄✆☎✈Ö✝☎❁×❊Ý❭Ü✟✞✆✠☛✡✼Ù✼Ø▲Ýq×✌☞✎✍✑✏ ✮❄■✍❋❊■✣❒P❃④➮✮❏✦ÏÐ❛➱❛◆P❃❒P❈❊✃①◗★✜✡✮Þ◗☞✣❫❐❁ç✱❈❊❃④➮✽❈åç✐■✍◆P■✧❃❋♥❒P◆P➱⑧❑⑧❖▲➮✣■❦❑✿❘❉▼✕✦✖➱❛Ï Ï④❏✦❋▲❑✁✯✲✥✸❡❐➤❏✦◆P■✧❃❒P■✍◆✽❏❲❅ ❒P❃Ò❛■➽◗❭■❦❏✦◆✽➮✽❈✵❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■❦◗✐❘▲❏❛◗❭■❦❑①➱❛❋➢❒P❈❊■➽◗❭ß❊❃ ◆P❃❒✸➱✦❮✈❋▲❏❲❒P❖❊◆✽❏✦Ï➩■✍Ò❛➱❛Ï❖⑧❒P❃➱❛❋✂ä✙✘✐▼✵■✍✃✧❖❊Ï④❏❲❒P❃❋❊Ð ❘❊❃➱❛Ï➱❛Ð❛❃④➮✍❏✦Ï➤◗❭■✍Ï■❦➮❫❒P❃➱❛❋➧❏✦❋▲❑➢◆P■✍ß❊◆P➱⑧❑⑧❖▲➮❫❒P❃➱❛❋✂❐❆✮Þ◗✱➮✍❏✦❋➢■✣ã➢➮✣❃■✍❋♥❒PÏ▼➧◗❭■❦❏✦◆✽➮✽❈✵❒P❈❊◆P➱❛❖❊Ð❛❈➢❒P❈❊■ ◗❭➱❛Ï❖⑧❒P❃➱❛❋⑩◗❭ß▲❏❛➮✣■✝➱✦❮❣➮✣➱❛✃✵ß❊Ï■✣é➯ß❊◆P➱❛❘❊Ï■✍✃①◗✍ä✑✘✸❏❛◗❭❃④➮✍❏✦Ï Ï▼❛❐✼❏ ✮Þ ➱❛ß➩■✍◆✽❏❲❒P■❦◗✵➱❛❋✫❏áß➩➱❛ß❊❖⑧❅ Ï④❏❲❒P❃➱❛❋à➱✦❮✡➮✍❏✦❋▲❑⑧❃④❑❊❏❲❒P■➧◗❭➱❛Ï❖⑧❒P❃➱❛❋▲◗✧➮✍❏✦Ï Ï■❦❑✫☎✽ô❉òPï❲ð①ïtí✍ï❲ð①ñ❫í❫ä Þ ➮✽❈❊◆P➱❛✃✵➱♥◗❭➱❛✃✵■❛❐✂ç✱❈❊❃④➮✽❈à❃④◗ ➮✣➱❛✃✵ß➩➱♥◗❭■❦❑➢➱✦❮✈❋❉❖❊✃✵■✍◆P➱❛❖▲◗✐Ð❛■✍❋❊■❦◗✍❐❉◆P■✍ß❊◆P■❦◗❭■✍❋♥❒✽◗✐❏✦❋➧■✍❋▲➮✣➱⑧❑⑧❃❋❊Ð✮➱✦❮✈❒P❈❊■➽ß❊◆P➱❛❘❊Ï■✍✃ ❏✦❋▲❑➧❏❛◗❇❅ ◗❭➱⑧➮✣❃④❏❲❒P■❦◗✐❃❒✖ç✱❃❒P❈✿❏★￾❊❒P❋❊■❦◗P◗✸Ò❲❏✦Ï❖❊■➽■✍Ò❲❏✦Ï❖▲❏❲❒P■❦❑☞❘❉▼①❒P❈❊■❩￾❊❒P❋❊■❦◗P◗✸❮➊❖❊❋▲➮❫❒P❃➱❛❋✂ä▲æ✸❈❊❃④◗❄￾❊❒P❋❊■❦◗P◗ Ò❲❏✦Ï❖❊■✛❑⑧■✣❒P■✍◆P✃✵❃❋❊■❦◗✐❒P❈❊■❣Ð❛➱❉➱⑧❑⑧❋❊■❦◗P◗✸❏✦❋▲❑➧❒P❈❊■✛◗❭❖❊◆PÒ❉❃Ò❲❏✦Ï✂❏✦❘❊❃ Ï ❃❒❇▼➢➱✦❮✈❒P❈❊■✛➮✽❈❊◆P➱❛✃✵➱♥◗❭➱❛✃✵■❛ä ✮❄■✍❋❊■✍◆✽❏✦Ï Ï▼❛❐❉✮Þ ◗❇❒✽❏✦◆❭❒✽◗✟❘❉▼✧❃❋❊❃❒P❃④❏✦Ï ❃❰✍❃❋❊Ð➽❒P❈❊■✖ß➩➱❛ß❊❖❊Ï④❏❲❒P❃➱❛❋①❏✦❋▲❑✵■✍Ò❲❏✦Ï❖▲❏❲❒P❃❋❊Ð❣❃❒✽◗✼➮✣➱❛◆❭❅ ◆P■❦◗❭ß➩➱❛❋▲❑⑧❃❋❊Ð ￾❊❒P❋❊■❦◗P◗❄Ò❲❏✦Ï❖❊■❦◗✍ä❀✘✐■✣❮➊➱❛◆P■✮❃❒❄❒P■✍◆P✃✵❃❋▲❏❲❒P■❦◗✍❐◆✮Þ 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Yi-Shin Chen and Cyrus Shahabi according to the survival ability for reproducing offspring. The offspring are generated through crossover and mutation processes and are used for replac ing some chromosomes in the population with a probability consistent with their fitness values. In other words, with the help of the fitness function te point out the correct direction, GA could construct better and bet ter chromo somes from the best partial genes of past samplings. Please see reference [17 for mathematical foundati n summary, Ga is composed of a fitness function, a population of chromo somes and three operators-selection, crossover and mutation. The parameter settings of the operators can be chosen depending on the applications or re ain unchanged even when the applications are varied. However, the fitness function and the coding met hod are required to be specially designed for each problem. The design of fitness function and encoding method for Yoda will 3 Overview The primary objective of a web-based recommendation system can be stated Problem 1. Suppose the item-set I= ii is an item presented in a web-site f and u is a user interactively navigating the Web-site. The recommendation problem is to obtain the wish-list lu e l, which is a list of items that are ranked based on u's interests In general, to acquire a wish-list for a user, a recommendation process goe through three steps/phases 1. Obtaining User Perceptions: Data about user perceptions such as naviga tion behaviors are collected. In some systems 8.7, these data need further processing for abstracting data which are used in the later phases 2. Ranking the Items: The predicted user interests are utilized to provide the predicted user wish-list 3. Adjusting user settings: The system acquires relevance feedback (or follow-up navigation behaviors)from the user and employs it to refine the user set tings/profiles, which represent the user perceptions On occa sion, this phase is integrated into phase one. Figure 1 illustrates the processing flow of Yoda. Suppose that music Cds are the items presented in a web-site. Yoda provides each active user a list of ecommended CDs by analyzing his/her navigation behaviors. To generate the recommendations, during an offline process, Yoda obtains experts'recom mendation(termed erperts'wish-Iists ) which could be from human experts, clusters of user evaluations, or clusters of navigation patterns. Later, dur ng the on- line recommendation process, the system first acquires the initial user profile, which is composed of a list of confidence values and a fuzzy

❼ ➳✸❵ ❸❡❜✦✇♥❵❚①❤✕✇♥❳✽❚✵❬✍❚❉❪①❤✈❝✦❩❇♠♥❞❍❜✦✇❉❬✍✇❉❬✍➝♥❵ ❏❛➮✍➮✣➱❛◆✽❑⑧❃❋❊Ð✮❒P➱✵❒P❈❊■✛◗❭❖❊◆PÒ❉❃Ò❲❏✦Ï✈❏✦❘❊❃ Ï ❃❒❇▼➢❮➊➱❛◆✖◆P■✍ß❊◆P➱⑧❑⑧❖▲➮✣❃❋❊Ð✵➱✦â❁◗❭ß❊◆P❃❋❊Ð▲ä❊æ✸❈❊■❣➱✦â❁◗❭ß❊◆P❃❋❊Ð➢❏✦◆P■ Ð❛■✍❋❊■✍◆✽❏❲❒P■❦❑✮❒P❈❊◆P➱❛❖❊Ð❛❈➧➮✣◆P➱♥◗P◗❭➱❲Ò❛■✍◆✼❏✦❋▲❑①✃✧❖⑧❒✽❏❲❒P❃➱❛❋➢ß❊◆P➱⑧➮✣■❦◗P◗❭■❦◗❍❏✦❋▲❑➧❏✦◆P■✖❖▲◗❭■❦❑✵❮➊➱❛◆✐◆P■✍ß❊Ï④❏❛➮❫❅ ❃❋❊Ð☞◗❭➱❛✃✵■✧➮✽❈❊◆P➱❛✃✵➱♥◗❭➱❛✃✵■❦◗✱❃❋✿❒P❈❊■✧ß➩➱❛ß❊❖❊Ï④❏❲❒P❃➱❛❋åç✱❃❒P❈á❏①ß❊◆P➱❛❘▲❏✦❘❊❃ Ï ❃❒❇▼✝➮✣➱❛❋▲◗❭❃④◗❇❒P■✍❋♥❒✡ç✱❃❒P❈ ❒P❈❊■✍❃ ◆✭￾❊❒P❋❊■❦◗P◗➽Ò❲❏✦Ï❖❊■❦◗✍ä➤èq❋á➱✦❒P❈❊■✍◆➽ç✐➱❛◆✽❑❊◗✍❐➤ç✱❃❒P❈✢❒P❈❊■①❈❊■✍Ïßá➱✦❮❍❒P❈❊■✻￾❊❒P❋❊■❦◗P◗❄❮➊❖❊❋▲➮❫❒P❃➱❛❋✢❒P➱ ß➩➱❛❃❋♥❒②➱❛❖⑧❒✈❒P❈❊■✸➮✣➱❛◆P◆P■❦➮❫❒✈❑⑧❃ ◆P■❦➮❫❒P❃➱❛❋✂❐✞✮Þ ➮✣➱❛❖❊Ï④❑✛➮✣➱❛❋▲◗❇❒P◆P❖▲➮❫❒✕❘➩■✣❒❭❒P■✍◆✟❏✦❋▲❑❣❘➩■✣❒❭❒P■✍◆✟➮✽❈❊◆P➱❛✃✵➱✦❅ ◗❭➱❛✃✵■❦◗✟❮➊◆P➱❛✃➫❒P❈❊■✡❘➩■❦◗❇❒✐ß▲❏✦◆❭❒P❃④❏✦Ï▲Ð❛■✍❋❊■❦◗✼➱✦❮✂ß▲❏❛◗❇❒✸◗P❏✦✃✵ß❊Ï ❃❋❊Ð♥◗✍ä✁￾✼Ï■❦❏❛◗❭■✖◗❭■✍■✡◆P■✣❮➊■✍◆P■✍❋▲➮✣■★✯ ✱✞❪✶✸ ❮➊➱❛◆✱✃①❏❲❒P❈❊■✍✃①❏❲❒P❃④➮✍❏✦Ï➤❮➊➱❛❖❊❋▲❑❊❏❲❒P❃➱❛❋▲◗✍ä èq❋✛◗❭❖❊✃✵✃①❏✦◆P▼❛❐✶✮Þ ❃④◗✈➮✣➱❛✃✵ß➩➱♥◗❭■❦❑➽➱✦❮⑧❏ ￾❊❒P❋❊■❦◗P◗❁❮➊❖❊❋▲➮❫❒P❃➱❛❋✂❐t❏✸ß➩➱❛ß❊❖❊Ï④❏❲❒P❃➱❛❋❣➱✦❮❊➮✽❈❊◆P➱❛✃✵➱✦❅ ◗❭➱❛✃✵■❦◗✕❏✦❋▲❑➽❒P❈❊◆P■✍■✐➱❛ß➩■✍◆✽❏❲❒P➱❛◆✽◗➤❅❁◗❭■✍Ï■❦➮❫❒P❃➱❛❋✂❐❲➮✣◆P➱♥◗P◗❭➱❲Ò❛■✍◆✂❏✦❋▲❑❣✃✧❖⑧❒✽❏❲❒P❃➱❛❋✂ä❲æ✸❈❊■❍ß▲❏✦◆✽❏✦✃✵■✣❒P■✍◆ ◗❭■✣❒❭❒P❃❋❊Ð♥◗✱➱✦❮✕❒P❈❊■❣➱❛ß➩■✍◆✽❏❲❒P➱❛◆✽◗✸➮✍❏✦❋✝❘➩■✛➮✽❈❊➱♥◗❭■✍❋✝❑⑧■✍ß➩■✍❋▲❑⑧❃❋❊Ð①➱❛❋☞❒P❈❊■✧❏✦ß❊ß❊Ï ❃④➮✍❏❲❒P❃➱❛❋▲◗✸➱❛◆✱◆P■✣❅ ✃①❏✦❃❋☞❖❊❋▲➮✽❈▲❏✦❋❊Ð❛■❦❑➧■✍Ò❛■✍❋➧ç✱❈❊■✍❋☞❒P❈❊■✛❏✦ß❊ß❊Ï ❃④➮✍❏❲❒P❃➱❛❋▲◗✸❏✦◆P■❄Ò❲❏✦◆P❃■❦❑❁ä❋✦✖➱❲ç✐■✍Ò❛■✍◆❦❐❛❒P❈❊■✭￾❊❒P❋❊■❦◗P◗ ❮➊❖❊❋▲➮❫❒P❃➱❛❋✵❏✦❋▲❑✛❒P❈❊■✱➮✣➱⑧❑⑧❃❋❊Ð❄✃✵■✣❒P❈❊➱⑧❑✮❏✦◆P■❍◆P■❦Ñ♥❖❊❃ ◆P■❦❑❣❒P➱❄❘➩■✱◗❭ß➩■❦➮✣❃④❏✦Ï Ï▼✧❑⑧■❦◗❭❃Ð❛❋❊■❦❑❣❮➊➱❛◆②■❦❏❛➮✽❈ ß❊◆P➱❛❘❊Ï■✍✃✝ä➤æ✸❈❊■✵❑⑧■❦◗❭❃Ð❛❋✢➱✦❮✑￾❊❒P❋❊■❦◗P◗✡❮➊❖❊❋▲➮❫❒P❃➱❛❋á❏✦❋▲❑å■✍❋▲➮✣➱⑧❑⑧❃❋❊Ð➧✃✵■✣❒P❈❊➱⑧❑å❮➊➱❛◆✡❂✼➱⑧❑❊❏➧ç✱❃ Ï Ï ❘➩■✛❑⑧■❦◗P➮✣◆P❃❘➩■❦❑➧❃❋å❆❉■❦➮❫❒P❃➱❛❋ ✲▲ä ❚❊ä ✂ ✄✆☎☎✂Ø✝☎✖Ý☛☎✟✞ æ✸❈❊■✡ß❊◆P❃✃①❏✦◆P▼✧➱❛❘❆❅❇■❦➮❫❒P❃Ò❛■✖➱✦❮✈❏❣ç✐■✍❘⑧❅❡❘▲❏❛◗❭■❦❑✵◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋①◗❭▼⑧◗❇❒P■✍✃ ➮✍❏✦❋①❘➩■❄◗❇❒✽❏❲❒P■❦❑ ❏❛◗✐❮➊➱❛Ï Ï➱❲ç✖◗✄✂ ✠✐òPï❛õ✟✏ ñ✣ð☛✡✌☞✡❆❉❖❊ß❊ß➩➱♥◗❭■❣❒P❈❊■ ✒➀îqñ✣ð✻✠❡í✍ñ✣î✎✍✑✏✓✒✕✔✗✖ ✔❍❃④◗✡❏✦❋✝❃❒P■✍✃✥ß❊◆P■❦◗❭■✍❋♥❒P■❦❑☞❃❋å❏✵ç✐■✍❘⑧❅q◗❭❃❒P■ ✘ ❏✦❋▲❑✑✙☞❃④◗❍❏✛❖▲◗❭■✍◆❍❃❋♥❒P■✍◆✽❏❛➮❫❒P❃Ò❛■✍Ï▼✮❋▲❏tÒ❉❃Ð♥❏❲❒P❃❋❊Ð➽❒P❈❊■✡ûá■✍❘⑧❅q◗❭❃❒P■❛ä❉æ✸❈❊■✡◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋ ß❊◆P➱❛❘❊Ï■✍✃✓❃④◗❣❒P➱å➱❛❘⑧❒✽❏✦❃❋á❒P❈❊■✢ó❁✒④íPô✙✠✼✏✒④í❫î✚✍✗✛✢✜✣✍▲❐✈ç✱❈❊❃④➮✽❈à❃④◗✧❏✿Ï ❃④◗❇❒✧➱✦❮✱❃❒P■✍✃①◗✛❒P❈▲❏❲❒✮❏✦◆P■ ◆✽❏✦❋❊Ó❛■❦❑➢❘▲❏❛◗❭■❦❑➧➱❛❋✤✙✕ü ◗✸❃❋♥❒P■✍◆P■❦◗❇❒✽◗✍ä èq❋åÐ❛■✍❋❊■✍◆✽❏✦Ï❯❐❊❒P➱➧❏❛➮✍Ñ♥❖❊❃ ◆P■✧❏①ç✱❃④◗❭❈⑧❅❡Ï ❃④◗❇❒✡❮➊➱❛◆❄❏➢❖▲◗❭■✍◆❦❐➩❏①◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋✿ß❊◆P➱⑧➮✣■❦◗P◗✖Ð❛➱❉■❦◗ ❒P❈❊◆P➱❛❖❊Ð❛❈➧❒P❈❊◆P■✍■❣◗❇❒P■✍ß▲◗✦✥tß❊❈▲❏❛◗❭■❦◗✄✂ ✱❛ä ✺❘⑧❒✽❏✦❃❋❊❃❋❊Ð★✧✡◗❭■✍◆✩￾②■✍◆✽➮✣■✍ß⑧❒P❃➱❛❋▲◗✄✂❄✹❄❏❲❒✽❏✡❏✦❘➩➱❛❖⑧❒②❖▲◗❭■✍◆✈ß➩■✍◆✽➮✣■✍ß⑧❒P❃➱❛❋▲◗✕◗❭❖▲➮✽❈✧❏❛◗✈❋▲❏tÒ❉❃Ð♥❏❲❅ ❒P❃➱❛❋❣❘➩■✍❈▲❏tÒ❉❃➱❛◆✽◗✂❏✦◆P■✼➮✣➱❛Ï Ï■❦➮❫❒P■❦❑❁ä✍èq❋✛◗❭➱❛✃✵■❍◗❭▼⑧◗❇❒P■✍✃①◗✑✯❱❊❐ ❪✶✸❡❐✣❒P❈❊■❦◗❭■❍❑❊❏❲❒✽❏✱❋❊■✍■❦❑❄❮➊❖❊◆❭❒P❈❊■✍◆ ß❊◆P➱⑧➮✣■❦◗P◗❭❃❋❊Ð✧❮➊➱❛◆✖❏✦❘▲◗❇❒P◆✽❏❛➮❫❒P❃❋❊Ð①❑❊❏❲❒✽❏✮ç✱❈❊❃④➮✽❈✝❏✦◆P■➽❖▲◗❭■❦❑➧❃❋☞❒P❈❊■❣Ï④❏❲❒P■✍◆✱ß❊❈▲❏❛◗❭■❦◗✍ä ✷⑧ä✎✪✖❏✦❋❊Ó❉❃❋❊Ð✮❒P❈❊■➽è❡❒P■✍✃①◗✄✂✻æ✸❈❊■➽ß❊◆P■❦❑⑧❃④➮❫❒P■❦❑➧❖▲◗❭■✍◆✱❃❋♥❒P■✍◆P■❦◗❇❒✽◗✱❏✦◆P■❄❖⑧❒P❃ Ï ❃❰✍■❦❑➧❒P➱✵ß❊◆P➱❲Ò❉❃④❑⑧■ ❒P❈❊■❣ß❊◆P■❦❑⑧❃④➮❫❒P■❦❑☞❖▲◗❭■✍◆✱ç✱❃④◗❭❈⑧❅❡Ï ❃④◗❇❒❦ä ❚❊ä Þ❑✤❅❇❖▲◗❇❒P❃❋❊Ð ❖▲◗❭■✍◆å◗❭■✣❒❭❒P❃❋❊Ð♥◗✄✂ æ✸❈❊■ ◗❭▼⑧◗❇❒P■✍✃✑❏❛➮✍Ñ♥❖❊❃ ◆P■❦◗☞◆P■✍Ï■✍Ò❲❏✦❋▲➮✣■á❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó ✜➊➱❛◆ ❮➊➱❛Ï Ï➱❲ç✸❅❡❖❊ßà❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋à❘➩■✍❈▲❏tÒ❉❃➱❛◆✽◗☞✣✡❮➊◆P➱❛✃✓❒P❈❊■➧❖▲◗❭■✍◆✵❏✦❋▲❑à■✍✃✵ß❊Ï➱❲▼⑧◗✛❃❒✧❒P➱✢◆P■✟￾▲❋❊■ ❒P❈❊■✡❖▲◗❭■✍◆✐◗❭■✣❒❭❒P❃❋❊Ð♥◗✦✥tß❊◆P➱✥￾▲Ï■❦◗✍❐❲ç✱❈❊❃④➮✽❈①◆P■✍ß❊◆P■❦◗❭■✍❋♥❒✼❒P❈❊■✖❖▲◗❭■✍◆❍ß➩■✍◆✽➮✣■✍ß⑧❒P❃➱❛❋▲◗✍ä ✺❋①➱⑧➮✍➮✍❏❲❅ ◗❭❃➱❛❋✂❐❉❒P❈❊❃④◗✱ß❊❈▲❏❛◗❭■➽❃④◗✱❃❋♥❒P■✍Ð❛◆✽❏❲❒P■❦❑➢❃❋♥❒P➱①ß❊❈▲❏❛◗❭■➽➱❛❋❊■❛ä ❃✕❃Ð❛❖❊◆P■★✱✡❃ Ï Ï❖▲◗❇❒P◆✽❏❲❒P■❦◗❍❒P❈❊■❄ß❊◆P➱⑧➮✣■❦◗P◗❭❃❋❊Ð✬✫▲➱❲ç✫➱✦❮✂❂✼➱⑧❑❊❏❊ä❊❆❉❖❊ß❊ß➩➱♥◗❭■✡❒P❈▲❏❲❒✸✃✧❖▲◗❭❃④➮❣●❄✹❄◗ ❏✦◆P■✱❒P❈❊■✡❃❒P■✍✃①◗✼ß❊◆P■❦◗❭■✍❋♥❒P■❦❑✵❃❋➧❏✛ç✐■✍❘⑧❅q◗❭❃❒P■❛ä✦❂✼➱⑧❑❊❏❣ß❊◆P➱❲Ò❉❃④❑⑧■❦◗✟■❦❏❛➮✽❈➢❏❛➮❫❒P❃Ò❛■✖❖▲◗❭■✍◆✐❏❣Ï ❃④◗❇❒✐➱✦❮ ◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑⑧■❦❑➯●❄✹❄◗✛❘❉▼ ❏✦❋▲❏✦Ï▼❉❰✍❃❋❊Ð✿❈❊❃④◗✦✥t❈❊■✍◆✛❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋ ❘➩■✍❈▲❏tÒ❉❃➱❛◆✽◗✍ä✂æ✈➱åÐ❛■✍❋❊■✍◆✽❏❲❒P■ ❒P❈❊■✐◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋▲◗✍❐❦❑⑧❖❊◆P❃❋❊Ð➽❏✦❋✛➱✥❜①❃❋❊■✐ß❊◆P➱⑧➮✣■❦◗P◗✍❐✣❂✼➱⑧❑❊❏✡➱❛❘⑧❒✽❏✦❃❋▲◗✈■✣é⑧ß➩■✍◆❭❒✽◗✍ü❦◆P■❦➮✣➱❛✃✮❅ ✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋❵✜➀❒P■✍◆P✃✵■❦❑ ñ✮✭✓✽▲ñ✣ò❫î❾í✰✯♥ó❁✒④íPô✙✠✼✏✒④í❫î❾í✣❫❐❊ç✱❈❊❃④➮✽❈✝➮✣➱❛❖❊Ï④❑➧❘➩■❄❮➊◆P➱❛✃ ❈❉❖❊✃①❏✦❋☞■✣é⑧ß➩■✍◆❭❒✽◗✍❐ ➮✣Ï❖▲◗❇❒P■✍◆✽◗✧➱✦❮❄❖▲◗❭■✍◆✮■✍Ò❲❏✦Ï❖▲❏❲❒P❃➱❛❋▲◗✍❐✕➱❛◆✵➮✣Ï❖▲◗❇❒P■✍◆✽◗✮➱✦❮✡❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋àß▲❏❲❒❭❒P■✍◆P❋▲◗✍ä✲✱✈❏❲❒P■✍◆❦❐②❑⑧❖❊◆❭❅ ❃❋❊Ð✵❒P❈❊■❣➱❛❋⑧❅❡Ï ❃❋❊■❣◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋☞ß❊◆P➱⑧➮✣■❦◗P◗✍❐❉❒P❈❊■✛◗❭▼⑧◗❇❒P■✍✃ ￾▲◆✽◗❇❒✡❏❛➮✍Ñ♥❖❊❃ ◆P■❦◗❍❒P❈❊■✛❃❋❊❃❒P❃④❏✦Ï ❖▲◗❭■✍◆➢ß❊◆P➱✥￾▲Ï■❛❐✼ç✱❈❊❃④➮✽❈✫❃④◗➢➮✣➱❛✃✵ß➩➱♥◗❭■❦❑ö➱✦❮✛❏ Ï ❃④◗❇❒➧➱✦❮❣➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■åÒ❲❏✦Ï❖❊■❦◗➢❏✦❋▲❑⑩❏ ❮➊❖❊❰✍❰✍▼

Improving User Profiles for E-Commerce by genetic algorithms Aggregati。n Update User Profile c1ass11cat⊥ Fig. 1. Processing Flow of Yoda t by softly classifying the user with clusters of navigation patterns. Note that the confidence values of the user toward ot her experts are not estimated t this step because the estimation process is comparably time consuming Subsequently, Yoda uses the user profile to generate the customized recor mendation(termed user wish-list) by weighted aggregation of the experts ish-lists. Thereafter, the sy stem improves and updates the background process by utilizing the follow-up user navigation behaviors and GA 4 System Design In this section, we provide a detailed description of Yoda's com ponents. Since phase I is based on our previous work[18, here we elaborate more on phase II and iii of yoda 4.1 Phase i- Obtaining User Perception Yoda uses the client-side tracking mechanism proposed in[ 6 to capture view- e, hit-Count, and sequen ce of visiting the web-pages (items) within a web- te. These features reflect users interests on items. To analy ze these features and infer the user interests, Yoda employs the Feature Matrices(FM) model which we introduced in 18. FM is a set of hyper-cube data structures that

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