Upload
others
View
0
Download
0
Embed Size (px)
Citation preview
資訊科技導論 PART IVINTRODUCTION TO INFORMATION TECHNOLOGIESTopic 1: Advanced Web TechniquesInstructor: I-Hsien Ting
RECOMMENDATION
RECOMMENDATION APPROACHES
Most of Recommendation Systems are developed for PersonalizationDifferent Approaches for Recommendation
Simple ApproachContent-based ApproachCollaborative FilteringHybrid Methods
SIMPLE APPROACH
Ratinge.g. John Doe gave the movie “Harry Potter” the rating of 7 (out of 10)Not-yet-rated problem??
User’s ProfileForm the preferences that identified by users
CONTENT-BASED METHODS
The content-based approach to recommendation has its roots in information retrieval and information filtering.Let Content(s) be an item profile
Keywords for text-based systemsThe importance of word kj in document dj is determined with some weighting measure wij
Features for graphical images. Audio streams, and video streams.
TF & IDFHow to measure the importance of keywords
TF (term-frequency)
IDF (inverse document frequency)
Weight
SOME PROBLEMS OF CONTENT-BASEDRECOMMENDATION
Limited Content AnalysisLimited to text-based documentsAutomatic feature extraction methods are much harder to apply to multimedia data.
Overspecializatione.g. A person with no experience with Greek cuisine would never receive a recommendation for even the greatest Greek restaurant in town.Item should not be recommended if they are too similar.
New User ProblemFew ratings
COLLABORATIVE RECOMMENDATION
Recommendation based on “Similar” usersTwo general classes
Memory-based (heuristic-based)Heuristic that make rating predictions based on the entire collection of previously rated items by the usersHow to measure the similarity is the key to generate recommendations
Model-basedThe collection of rating to learn a model, which is then used to make rating prediction.
Probabilistic approachLinear regressionBayesian modelData mining approaches: k-mean clustering……
SIMILARITY
Pearson Correlation Coefficient
Cosine-based Approach
Mean-squared difference
SOME PROBLEMS OF COLLABORATIVERECOMMENDATION
New User ProblemNew Item ProblemSparsity
It is not enough to generate recommendation only according to rating informationDemographic segments
The gender, age, area code, education and employment information
HYBRID RECOMMENDATION METHODS
For more accurate recommendationsDifferent ways
Implementing collaborative and content-based methods separately and combining their predictions
DailyLearner SystemIncorporating some content-based characteristics into a collaborative approach
Fab and Collaboration via contentIncorporating some collaborative characteristics into a content-based approachConstructing a general unifying model that incorporates both content-based and collaborative characteristics
RECOMMENDATION SYSTEMS: NEWS DUDE
Billsus and Pazzani, “A hybrid user model for news story classification,” Conf. on User Modeling, 1999.A content-based approach for filtering news.
A short term interest profile that record recently read news.A long term interest described as a probability model.
An article first goes through the short term interest profile, followed by long term interest.Experimental results show that the hybrid approach perform better than either model.
RECOMMENDATION SYSTEMS: FIREFLY
Shardanand and Maes, “Social information filtering: Algorithms for automating ‘word of mouth’., CHI95.A collaborative approach for filtering music.An early version is called Ringo.
RECOMMENDATION SYSTEMS: WEBWATCHER
Joachims, Freitag, Mitchell, “WebWatcher: A tour guide for the World Wide Web,” Conf. on AI, 1997.Combine content-based and collaborative approaches to weigh hyperlinks in a given page.The core is a content-based prediction.Users have to specify its goal of browsing at the beginning.The content of a hyperlink includes
Web page text.Users’ descriptive keywords.
The result has shown to be as good as human experts.
RECOMMENDATION SYSTEMS: CLIXSMART
Perkowitz and Etzioni, “Adaptive Web sites: An AI challenge,” IJCAI97.A combination of content-based and collaborative recommendation for personalized TV guide.Serving more than 20,000 users in Ireland and Great Britain.Each program is featured by name, channel, airtime, genre, country of origin, cast, studio, director, writer, etc.Launched since 1999, there have been more than 20,000 registered users.Through questionnaires, users express high degree of satisfaction.Through precision measures, it is found that collaborative filtering behaves better than content-based, which again is better than randomization.
SEMANTIC WEB
SEMANTIC WEB
A self-explained WebWeb pages are design to be read by people, but not machineExample: Search for a low price flight ticketExample: Make Reservations to Library
SEMANTIC WEB & NON-SEMANTIC WEB
Non-semantic web<item>cat</item>
Semantic Web<animal Kingdom="Animalia" Phylum="Chordata" Class="Mammalia" Order="Carnivora" Family="Felidae" Genus="Felis">Cat</animal>
METADATA IN HTML<meta name="keywords" content="computing, computer studies, computer“><meta name="description" content="Cheap widgets for sale“><meta name="author" content="Hack's Hardware">
RDF-AN EXTENTION OF METADATA
RDF: Resources Description FrameworkXML is a simply RDF A Simple RDF
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:terms="http://purl.org/dc/terms/"> <rdf:Description rdf:about="urn:x-states:New%20York"> <terms:alternative>NY</terms:alternative>
</rdf:Description> </rdf:RDF>
OWLOWL: Web Ontology Language
A Family of Knowledge Representation Language for authoring ontologies
OWL Web Ontology Language Overviewhttp://www.w3.org/TR/owl-features/
<owl:Class rdf:ID="Burgundy"> ... <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource=“#hasSugar” /> <owl:hasValue rdf:resource=“#Dry” /> </owl:Restriction></rdfs:subClassOf>
</owl:Class>
SPARQLAn RDF Query Language
PREFIX abc: <http://example.com/exampleOntology#> SELECT ?capital ?country WHERE { ?x abc:cityname ?capital ; abc:isCapitalOf ?y . ?y abc:countryname ?country ; abc:isInContinent abc:Africa .}
LINKING OPEN DATA DIAGRAM
Creating openly accessible, and interlinked, RDF Data on the Web.
http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
SEMANTIC WEB EXAMPLES
BibServhttp://www.bibserv.org/
ESP Gamehttp://www.espgame.org/