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. 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❒P❈❊■✱❑❊❏❲❒✽❏✦❘▲❏❛◗❭■❍ç✱❃❒P❈✧❒P❈❊➱♥◗❭■✸❃❋✛❒P❈❊■✸❖▲◗❭■✍◆❦ü ◗✕ß❊◆P➱✥￾▲Ï■❛ä✞✦✖■✍❋▲➮✣■❛❐t❒P❈❊■✱❏❛➮✍➮✣❖❊◆✽❏❛➮✣▼❄➱✦❮▲❒P❈❊■✸❖▲◗❭■✍◆❦ü ◗ ß❊◆P➱✥￾▲Ï■❣❃④◗✖Ò❛■✍◆P▼➧❃✃✵ß➩➱❛◆❭❒✽❏✦❋♥❒❦ä▲æ✈➱➢Ó❛■✍■✍ß☞❒P❈❊■✛❖▲◗❭■✍◆✡ß❊◆P➱✥￾▲Ï■✛❏❛➮✍➮✣❖❊◆✽❏❲❒P■❛❐⑧Ò❲❏✦◆P❃➱❛❖▲◗✱Ï■❦❏✦◆P❋❊❃❋❊Ð ❒P■❦➮✽❈❊❋❊❃④Ñ♥❖❊■❦◗✍❐✈◗❭❖▲➮✽❈à❏❛◗★✧✢☛✞✩❛ñ❫í✓✒✪☛✞✝✫☎✟✏☛tí✽í✓✒✍✐ñ✣ò✽í❫❐✎✝➤ñ✣ì⑧ò✖☛✞✏✑✝➤ñ✣î❯ó❍ï❲ò✖✬tí❫❐✕❏✦❋▲❑à÷♥ñ✟✝➤ñ✣î✡✒✪☎✚☛✞✏÷♥ï✞✠ ò✓✒➀î➊ô❉ð✧í✭✜✡✮Þ✣❫❐➩❏✦◆P■❄❖⑧❒P❃ Ï ❃❰✍■❦❑☞❮➊➱❛◆✱◆P■✍Ò❉❃④◗❭❃❋❊Ð✮❖▲◗❭■✍◆✱ß❊◆P➱✥￾▲Ï■❦◗✰✯ ✱✄✲✞✳✴✱✶✵❊❐ ✷✤✸❡ä ✹✡■❦◗❭ß❊❃❒P■❄❒P❈❊■❦◗❭■➽❃✃✵ß❊◆P➱❲Ò❛■✍✃✵■✍❋♥❒✽◗✍❐♥❒P❈❊❃④◗✱❏✦ß❊ß❊◆P➱♥❏❛➮✽❈➢❈▲❏❛◗✸◗❭■✍Ò❛■✍◆✽❏✦Ï➩➱✦❒P❈❊■✍◆✱ç✐■❦❏✦Ó❉❋❊■❦◗P◗❭■❦◗✍ä ✺❋❊■➽❃④◗★☎✽ï✞✝▲îqñ✟✝▲î✎✏✒➀ð✻✒➀î✼☛❲î✡✒❾ï✞✝▲❐▲❃❯ä ■❛ä ❐❊Ï■✣é⑧❃④➮✍❏✦Ï➤❮➊◆✽❏✦Ð❛✃✵■✍❋♥❒✸✃✵■✣❒P❈❊➱⑧❑❊◗✖➮✍❏✦❋✝➱❛❋❊Ï▼➢❘➩■✛❏✦ß❊ß❊Ï ❃■❦❑ ❒P➱✝❒P■✣é❉❒✧➮✣➱❛❋♥❒P■✍❋♥❒❦ä✂æ✸❈❊■①➱✦❒P❈❊■✍◆✛❃④◗➢ï✞✗❲ñ✣ò✓✠❡í✡✽▲ñ☞☎✟✒✪☛✞✏✒✾✶☛❲î✡✒❾ï✞✝▲❐②❃❯ä ■❛ä ❐✂❖▲◗❭■✍◆✽◗❣➮✍❏✦❋ ➱❛❋❊Ï▼å➱❛❘⑧❒✽❏✦❃❋ ❒P❈❊■➢❃❋⑧❮➊➱❛◆P✃①❏❲❒P❃➱❛❋á❃❋▲❑⑧❃④➮✍❏❲❒P■❦❑ ❃❋á❒P❈❊■✍❃ ◆✛ß❊◆P➱✥￾▲Ï■❦◗✛❏✦❋▲❑á❈▲❏tÒ❛■✵❋❊➱å➮✽❈▲❏✦❋▲➮✣■✵➱✦❮✸■✣é⑧ß❊Ï➱❛◆P❃❋❊Ð ❋❊■✍ç✙❃❋⑧❮➊➱❛◆P✃①❏❲❒P❃➱❛❋✿❒P❈❊■✍▼å✃✵❃Ð❛❈♥❒❣❑⑧■❦◗❭❃ ◆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 ❃■✍◆✧ç✐➱❛◆PÓ❫✯ ✱✟✸❡❐✕ç✐■➧❃❋♥❒P◆P➱⑧❑⑧❖▲➮✣■❦❑ ❏✢❈❉▼❉❘❊◆P❃④❑à◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋ ◗❭▼⑧◗❇❒P■✍✃ ❅❵❴➩ï✶✌❉☛❲❐②ç✱❈❊❃④➮✽❈ö◗❭❃✃✧❖❊Ï❒✽❏✦❋❊■✍➱❛❖▲◗❭Ï▼á❖⑧❒P❃ Ï ❃❰✍■❦◗✧❒P❈❊■✝❏❛❑⑧Ò❲❏✦❋♥❒✽❏✦Ð❛■❦◗✛➱✦❮✻☎✟✏ì❉í❫îqñ✣ò✓✒✔✝❉÷✦❐✛☎✽ï✞✝▲îqñ✟✝▲î ☛✞✝✴☛✞✏✩tí✓✒④í❫❐▲❏✦❋▲❑❵☎✽ï✞✏✔✏☛❛õ✽ï❲ò✖☛❲îqñ❄✍✑✏îqñ✣ò✓✒✔✝❉÷✕✜❡●❄❃❁✣✐❏✦ß❊ß❊◆P➱♥❏❛➮✽❈❊■❦◗✍ä✙✘✸❏❛◗❭❃④➮✍❏✦Ï Ï▼❛❐❛❂✼➱⑧❑❊❏✮❃④◗✱❏✛❒❇ç✐➱✦❅ ◗❇❒P■✍ß✿❏✦ß❊ß❊◆P➱♥❏❛➮✽❈➧◆P■❦➮✣➱❛✃✵✃✵■✍❋▲❑❊❏❲❒P❃➱❛❋☞◗❭▼⑧◗❇❒P■✍✃✝ä❛✘✸❏❛◗❭❃④➮✍❏✦Ï Ï▼❛❐⑧❑⑧❖❊◆P❃❋❊Ð✮❒P❈❊■❣➱✥❜①❃❋❊■❣ß❊◆P➱⑧➮✣■❦◗P◗✍❐