Recommender system in Information Retrieval system.ppt

IRS

1. Recommender systemsRecommender systems

2. OverviewOverview
•DefinitionDefinition
•Ways its usedWays its used
•ProblemsProblems
•MaintenanceMaintenance
•The futureThe future

3. What is it?What is it?
•Recommender systems are a technological Recommender systems are a technological
proxy for a social process.proxy for a social process.
•Recommender systems are a way of Recommender systems are a way of
suggesting like or similar items and ideas to a suggesting like or similar items and ideas to a
users specific way of thinking.users specific way of thinking.
•Recommender systems try to automate Recommender systems try to automate
aspects of a completely different information aspects of a completely different information
discovery model where people try to find discovery model where people try to find
other people with similar tastes and then ask other people with similar tastes and then ask
them to suggest new things.them to suggest new things.

4. ExampleExample
•Customer ACustomer A
–Buys Metalica CDBuys Metalica CD
–Buys Megadeth CDBuys Megadeth CD
•Customer BCustomer B
–Does search on MetalicaDoes search on Metalica
–Recommender system Recommender system
suggests Megadeth from suggests Megadeth from
data collected from data collected from
customer Acustomer A

5. Motivation for Recommender Motivation for Recommender
SystemsSystems
•Automates quotes like:Automates quotes like:
–"I like this book; you might be interested in "I like this book; you might be interested in
it" it"
–"I saw this movie, you’ll like it“"I saw this movie, you’ll like it“
–"Don’t go see that movie!" "Don’t go see that movie!"

6. Further MotivationFurther Motivation
•Many of the top commerce sites use Many of the top commerce sites use
recommender systems to improve sales.recommender systems to improve sales.
•Users may find new books, music, or Users may find new books, music, or
movies that was previously unknown to movies that was previously unknown to
them.them.
•Also can find the opposite for e.g.: Also can find the opposite for e.g.:
movies or music that will definitely not movies or music that will definitely not
be enjoyed.be enjoyed.

7. Where is it used?Where is it used?
•Massive E-commerce sites use this tool Massive E-commerce sites use this tool
to suggest other items a consumer may to suggest other items a consumer may
want to purchasewant to purchase
•Web personalizationWeb personalization

8. Ways its usedWays its used
•Survey’s filled out by past users for the Survey’s filled out by past users for the
use of new usersuse of new users
•Search-style AlgorithmsSearch-style Algorithms
•Genre matchingGenre matching
•Past purchase queryingPast purchase querying

9. Recommender System TypesRecommender System Types
•Collaborative/Social-filtering systemCollaborative/Social-filtering system – aggregation of – aggregation of
consumers’ preferences and recommendations to consumers’ preferences and recommendations to
other users based on similarity in behavioral patternsother users based on similarity in behavioral patterns
•Content-based systemContent-based system – supervised machine learning – supervised machine learning
used to induce a classifier to discriminate between used to induce a classifier to discriminate between
interesting and uninteresting items for the userinteresting and uninteresting items for the user
•Knowledge-based systemKnowledge-based system – knowledge about users – knowledge about users
and products used to reason what meets the user’s and products used to reason what meets the user’s
requirements, using discrimination tree, decision requirements, using discrimination tree, decision
support tools, case-based reasoning (CBR)support tools, case-based reasoning (CBR)

10. Content-based Collaborative Content-based Collaborative
Information FilteringInformation Filtering
•Relevance feedbackRelevance feedback – positive/negative – positive/negative
prototypes.prototypes.
•Feature selectionFeature selection – removal of non- – removal of non-
informative terms.informative terms.
•Learning to recommendLearning to recommend – agent counts – agent counts
with 2 matrices; user vs. category matrix with 2 matrices; user vs. category matrix
(for successful classification) and user’s (for successful classification) and user’s
recommendation factor (1 to 5) or binary.recommendation factor (1 to 5) or binary.

11. products
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12. ExamplesExamples
Amazon.com Books, movies, musicBooks, movies, music
CDNOW.com MusicMusic
Ebay.com (feedback (feedback
forms)forms)
AnythingAnything
Reel.com MoviesMovies
Barnes & Noble BooksBooks

13. ProblemsProblems
•Inconclusive user feedback formsInconclusive user feedback forms
•Finding users to take the feedback surveysFinding users to take the feedback surveys
•Weak AlgorithmsWeak Algorithms
•Poor resultsPoor results
•Poor DataPoor Data
•Lack of DataLack of Data
•Privacy Control (May NOT explicitly Privacy Control (May NOT explicitly
collaborate with recipients)collaborate with recipients)

14. MaintenanceMaintenance
•CostlyCostly
•Information becomes outdatedInformation becomes outdated
•Information quantity (large, disk space Information quantity (large, disk space
expansion)expansion)

15. The Future of Recommender The Future of Recommender
SystemsSystems
•Extract implicit negative ratings through Extract implicit negative ratings through
the analysis of returned item.the analysis of returned item.
•How to integrate community with
recommendations
•Recommender systems will be used in
the future to predict demand for
products, enabling earlier
communication back the supply chain.

16. ResourcesResources
•http://www.acm.org/cacm/MAR97/resnick.htmlhttp://www.acm.org/cacm/MAR97/resnick.html
•http://www.ercim.org/publication/ws-http://www.ercim.org/publication/ws-
proceedings/DelNoe02/proceedings/DelNoe02/
CliffordLynchAbstract.pdfCliffordLynchAbstract.pdf
•http://people.cs.vt.edu/~ramakris/papers/http://people.cs.vt.edu/~ramakris/papers/
ppp.pdfppp.pdf
•http://www.sims.berkeley.edu/~sinha/talks/http://www.sims.berkeley.edu/~sinha/talks/
UMD-Rashmi.pdfUMD-Rashmi.pdf

17. Resources continuedResources continued
•http://www.cs.umn.edu/Research/http://www.cs.umn.edu/Research/
GroupLens/papers/pdf/ec-99.pdfGroupLens/papers/pdf/ec-99.pdf
•http://www.rashmisinha.com/talks/http://www.rashmisinha.com/talks/
Recommenders-SIGIR.pdfRecommenders-SIGIR.pdf
•http://www.grouplens.org/papers/pdf/http://www.grouplens.org/papers/pdf/
slides-1.pdfslides-1.pdf