Collaborative Filtering & Content-Based Recommending. CS 293S. T. Yang Slides based on R. Mooney at UT Austin

Transcription

1 ollaboative Filteing & ontent-based Recommending S 293S. T. Yang Slides based on R. Mooney at UT Astin 1

2 Recommendation Systems Systems fo ecommending items (e.g. books, movies, msic, web pages, newsgop messages) to ses based on examples of thei pefeences. Amazon, Netflix. Incease sales at on-line stoes. Basic appoaches to ecommending: ollaboative Filteing (a.k.a. social filteing) ontent-based Instances of pesonalization softwae. adapting to the individal needs, inteests, and pefeences of each se with ecommending, filteing, & pedicting 2

3 Pocess of Book Recommendation Red Mas Fond ation Jassic Pak Lost Wold Machine Leaning Use Pofile 2001 Neomance 2010 Diffeence Engine 3

4 ollaboative Filteing Maintain a database of many ses atings of a vaiety of items. Fo a given se, find othe simila ses whose atings stongly coelate with the cent se. Recommend items ated highly by these simila ses, bt not ated by the cent se. Almost all existing commecial ecommendes se this appoach (e.g. Amazon). Use ating? Use ating Use ating Use ating Use ating Use ating Item ecommendation 4

5 ollaboative Filteing Use Database A 9 B 3 Z 5 A B 9 Z 10 A 5 B 3 Z 7 A B 8 Z A 6 B 4 Z A 10 B Z 1 oelation Match A 9 B 3 Z 5 A 10 B Z 1 Active Use A 9 B 3.. Z 5 Extact Recommendations 5

6 ollaboative Filteing Method 1. Weight all ses with espect to similaity with the active se. 2. Select a sbset of the ses (neighbos) to se as pedictos. 3. Nomalize atings and compte a pediction fom a weighted combination of the selected neighbos atings. 4. Pesent items with highest pedicted atings as ecommendations. 6

7 Find ses with simila atings/inteests Use Database A 9 B 3 Z 5 A B 9 Z 10 A 5 B 3 Z 7 A B 8 Z A 6 B 4 Z A 10 B Z 1 Which ses have simila atings? Active Use A 9 B 3.. Z 5 a 7

8 Similaity Weighting Similaity of two ating vectos fo active se, a, and anothe se,. Peason coelation coefficient a, = cova( a, s s a a cosine similaity fomla c ) a and ae the atings vectos fo the m items ated by both a and Use Database A 9 B 3 Z 5 A B 9 Z 10 A 5 B 3 Z 7 A B 8 Z A 6 B 4 Z A 10 B Z 1 8

9 9 Definition: ovaiance and Standad Deviation ovaiance: Standad Deviation: Peason coelation coefficient m m i i a i a a å = - - = 1,, ) )( ( ), cova( m m i i x x å = = 1, m m i x i x x å = - = 1 2, ) ( s ), osine( ), cova(, a a a a c a - - = = s s

10 Neighbo Selection Fo a given active se, a, select coelated ses to seve as soce of pedictions. Standad appoach is to se the most simila n ses,, based on similaity weights, w a, Altenate appoach is to inclde all ses whose similaity weight is above a given theshold. Sim( a, )> t a 10

11 Significance Weighting Impotant not to tst coelations based on vey few co-ated items. Inclde significance weights, s a,, based on nmbe of co-ated items, m. s c w a, = a, a, ïì 1if = í m m if ïî 50 s a, m > 50 ïü 50ý ïþ 11

12 Rating Pediction (Vesion 0) Pedict a ating, p a,i, fo each item i, fo active se, a, by sing the n selected neighbo ses, Î {1,2, n}. Weight ses atings contibtion by thei similaity to the active se. p a, i n å = 1 = n w å = 1 a, w a,, i Use a Item i 12

13 Rating Pediction (Vesion 1) Pedict a ating, p a,i, fo each item i, fo active se, a, by sing the n selected neighbo ses, Î {1,2, n}. To accont fo ses diffeent atings levels, base pedictions on diffeences fom a se s aveage ating. Weight ses atings contibtion by thei similaity to the active se. p a, i n å w a, = 1 = a + n å = 1 ( w, i a, - ) Use a Item i 13

14 Poblems with ollaboative Filteing old Stat: Thee needs to be enogh othe ses aleady in the system to find a match. Spasity: If thee ae many items to be ecommended, even if thee ae many ses, the se/atings matix is spase, and it is had to find ses that have ated the same items. Fist Rate: annot ecommend an item that has not been peviosly ated. New items, esoteic items Poplaity Bias: annot ecommend items to someone with niqe tastes. Tends to ecommend popla items. 14

15 Recommendation vs Web Ranking Text ontent Link poplaity Use click data ontent Use ating Web page anking Item ecommendation 15

16 ontent-based Recommendation Recommendations ae based on infomation on the content of items athe than on othe ses opinions. Less dependence fo data on othe ses. Able to ecommend to ses with niqe tastes. Able to ecommend new and npopla items No fist-ate poblem. No cold-stat o spasity poblems.. 16

17 Example: LIBRA System Amazon Book Pages Infomation Extaction LIBRA Database Rated Examples Recommendations Machine Leaning Leane 1.~~~~~~ 2.~~~~~~~ 3.~~~~~ : : : Pedicto Use Pofile Uses infomation Atho Title Editoial Reviews stome omments Sbject tems Related athos Related titles 17

18 ombining ontent and ollaboation ontent-based and collaboative methods have complementay stengths and weaknesses. ombine methods to obtain the best of both. Vaios hybid appoaches: Apply both methods and combine ecommendations. Use collaboative data as content. Use content-based pedicto as anothe collaboato. Use content-based pedicto to complete collaboative data. 18

19 ontent-boosted ollaboative Filteing EachMovie Web awle IMDb Use Ratings Matix (Spase) Movie ontent Database ontent-based Pedicto Fll Use Ratings Matix Active Use Ratings ollaboative Filteing Recommendations 19

20 ontent-boosted ollaboative Filteing Use-atings Vecto ontent-based Pedicto Taining Examples Use-ated Items Unated Items Items with Pedicted Ratings Psedo Use-atings Vecto 20

21 ontent-boosted ollaboative Filteing Use Ratings Matix ontent-based Pedicto Psedo Use Ratings Matix ompte psedo se atings matix Fll matix appoximates actal fll se atings matix Pefom collaboative filteing Using Peason co. between psedo se-ating vectos 21

22 onclsions Recommending and pesonalization ae impotant appoaches to combating infomation ove-load. Machine Leaning is an impotant pat of systems fo these tasks. ollaboative filteing has poblems. ontent-based methods addess these poblems (bt have poblems of thei own). Integating both is best. 22