Baudisch Dissertation Dynamic Information Filtering
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Baudisch Dissertation Dynamic Information Filtering Baudisch Dissertation Dynamic Information Filtering Presentation Transcript

  • Dynamic information filtering Patrick Baudisch Xerox PARC March 26, 2001
  • Contents
    • Introduction
    • Requirements and related work
    • The TV Scout
      • …as a retrieval system
      • …and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
    • Introduction
    • Requirements and related work
    • The TV Scout
      • … as a retrieval system
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • Motivation: Information overload
    • Too many
      • research papers
      • books
      • movies
      • web pages
      • even TV programs!
    • Goal: alleviate information overload
  • IF, IR, and dynamic filtering
    • Analytic information seeking strategies
      • Retrieval (IR) changing interests, stable database
      • Filtering (IF) changing sources, stable interests
    • Many application fit in
      • dictionaries => IR
      • music => IF
    • Others fit into neither niche
      • High source and need change rate
      • Example stock market
      • [Oard 96]: “Grand challenge”
    Dynamic information filtering Filtering Retrieval Information source change rate Information need change rate
  • Objective of dynamic filtering
    • Adaptation speed is crucial
      • ( user profile = interest ) is crucial for filtering accuracy
      • Interest changes: ( profile  interest ) => filtering quality drops
      • Adapt profile as fast as possible
    • Subject of this thesis: Filtering architecture for maximum adaptation speed
    • Introduction
    • Requirements and related work
    • The TV Scout
      • … as a retrieval system
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • Requirements
    • Requirement 1: Exhaustiveness (arbitrary interests)
      • (King and Sacramento), but not (King and Queen), INFOS [Mock 96]
    • Requirement 2: Output style (single ranking preferred)
      • Boolean output, Info. Lens [Malone 87]; Categories, SIFT [Yan 95]
    • Requirements 3-5: Adapt to interest changes
    Gradual change [Belkin 92, Baclace 91, Lang 95, ...] Slowly (caused by process) Repetitive change [Allen 90, Loeb 92, Kay 95, …] Abrupt change [Marchionini 95, Lam 96, Frisse 89…] Rapidly (caused by event) Temporary Permanent
  • R3: Learning from relevance feedback time [Jennings 91, p.207] actual interests
    • Newt [Sheth and Maes 93]
    • WebMate [ Chen and K. Sycara ]
    • GroupLens [Konstan et al 97]
    user profile error delayed profile error interest
  • R4: Limitations of manual profile editing
    • Rule-based systems
    • Information Lens [Malone et al 87]
    • ISCREEN [Pollock 88]
    • INFOSCOPE [Fischer 91]
    Problems with gradual changes user interest error delayed reaction
  • Resulting design guideline
    • Build a filtering system that allows
      • learning from relevance feedback (for gradual changes)
      • users to edit their profiles directly (for abrupt changes)
    • and
      • that uses a “meaningful” model for the user profiles, so that users understand how to edit them
    • Introduction
    • Requirements and related work
    • The TV Scout
      • … as a retrieval system
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • Query Frame Content frame
  • Q1. select a query Best match Exact match
  • Q2. read & retain program descriptions program description list program description table retention menus … print them out, take them home video labels laundry list
  • Q3. suggestions suggest queries
    • Introduction
    • Requirements and related work
    • The TV Scout
      • … as a retrieval system
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • P+. Exact match profiles channel profile editor viewing time profile editor
  • Best match profile (QuerySet profile) QuerySet Profile: Personal program per single mouse click QuerySet profile editor (Expert mode)
  • Summary TV Scout interface with starting page viewing time profile editor channel profile editor query menus QSA menu text search program description list program description table suggest queries QSA profile editor QSA profile editor (experts) retention menus video labels laundry list
  • Incremental usage S1 U1 T1 system provides user writes queries (one shot state) bookmarks (reuse state) user defines system suggests S2 U2 T2 system compiles QSA profile (filtering state) S3 T  user updates system learns T3 U3 start
  • Studies done on the TV Scout so far
    • Comparison of individual query classes
      • > 13,000 registered users
      • Predefined queries (genres) covered most interests
      • Text search for what genres do not cover
        • Search for actors, series, topics
      • “Opinion leader” recommendation was 5 th most popular query
    • Long term study still outstanding
    • Introduction
    • Requirements and related work
    • The TV Scout
      • The TV Scout as a retrieval system…
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • QuerySet profile vs. other user profiles
    • Queries in QSA profile intended to represent different interests
      • != query representation nodes
      • ! = concepts (or facets ) that are part of a query/interest.
      • != IR query that represents a single interest only
    e.g. news, sports, Comedy shows How does user like news compared to sports…? This is not (necessarily) an inference network r 1 r m d 2 r 3 r 2 user profile d j d 1 d j-1 … … QSA profile q 1 A q n …
  • Objective of that decomposition
    • Several interests changes can be handled with minor profile changes
      • “ I am not in the mood for action movies today”
      • “ My taste in action movies has changed”
      • => Update only query weight in aggregation function Benefit: all queries remain unaffected
      • Edit only action movies query Benefit: all other queries remain unaffected
  • Make queries correspond to interests
    • Selection principle
      • Make a query what will change as a whole
      • It is interests that change
      • => Use queries corresponding to interests
    • Negative examples
      • Data fusion (e.g. [Fox 94, Lee 97]) => redundancy
      • Automated collaborative filtering => overlap
    • Positive example:
      • The Incremental usage supported by QSA systems: Use as query, then bookmark, then use as profile
    queries (one shot state) S1 U1 T1 user defines system suggests S2 U2 T2 system compiles QSA profile (filtering state) S3 T  system provides user writes bookmarks (reuse state) user updates system learns T3 U3 start
    • Introduction
    • Requirements and related work
    • The TV Scout
      • The TV Scout as a retrieval system…
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • How to build QSA systems? Reuse! Sybase, FreeWAIS, Print import, <more> IR/IF subsystem running the aggregation function Query-executing IR/IF subsystem Post-conversion Post-conversion relevance ratings IR/IF subsystem running the aggregation function Re-post-conversion Re-pre-conversion relevance feedback Re-post-conversion query feedback aggregation feedback Re-pre-conversion Pre-conversion query ratings output rating Pre-conversion Query-executing IR/IF subsystem
  • Aggregation subsystem
    • Example
      • User profile = {action movies, comedies, Tips by Lars}
      • Aggregation: turn these three rankings into a single ranking
      • Is a programs {0.4 action movie, 0.3 comedy, “excellent” by Lars} better than {0 action movie, 0.8 comedy, “ok” by Lars}?
    • Notion of tradeoffs similar to IR/IF systems on term frequencies
      • Query = {“information”, “retrieval”}
      • Is a web page {0.4 information, 0.3 retrieval} better than another web page {0 information, 0.8 retrieval}?
    • => Reuse IR/IF systems
    • Weighted request and indexing retrieval model
      • Output rating(object) = Sum of query ratings
      • TV Scout: Overlap between queries was small enough => This model is sufficient
    • Introduction
    • Requirements and related work
    • The TV Scout
      • The TV Scout as a retrieval system…
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • Simple case: “Rate a query”
    • What is the general concept behind profile editors?
    • Rate a query as a whole “How do you like science fiction movies”?
    • => This is fast, because users can take experience with and expectations about query into account
    • But what if the user loves news programs, but wants only a few top-ranked ones? (redundancy between news)
  • General case: “Rate a set”
    • Generalization
      • Ask user to rate arbitrary set of objects
      • Example “How do you like: {Back to the future, Brazil, Blade runner, 1984…Metropolis}?
    • User-aggregated relevance feedback
      • The user mentally assigns a rating to each object
      • The user aggregates these and tells the system the result
      • This save effort for communicating individual ratings
    • Benefit
      • “Rate a query” is a special case of “Rate a set”
      • This makes both compatible with relevance feedback
  • Combine both
    • Goal: find a way
      • as simple and fast as “rate a query”
      • as flexible as “rate a set”
    • Solution
      • Use top and bottom ranks of queries (and others)
      • Extensible to arbitrary ranks -> Histogram-based interfaces
    “ How much do you like top-ranked news programs?” “ How much do you like bottom-ranked news programs?”
  • Profile editor framework Skip all 2. Dead Poets Society 1. Bayern-Manchester 2. Amazons on Mars ------------------------------- 2. Le Grand Bleu 1. Sat1 ran Skip paintable interfaces   Query-wise preferable if few queries (e.g. query inserted) Property-wise preferable if many queries (e.g. mood change) few URF samples (simplicity): form-based interfaces many URF samples (accuracy): histogram-based interfaces   history B. Hills Soap Comedy M. Arts Action movies Information Schwarz.. Simpsons M.A.S.H. Sports Basket ball C. music Theater Golf Series Series undo save execute Action movies Information Sports B. Hills Soap M.A.S.H. C. music Theater M. Arts Schwarz.. Basket ball Golf Series Series Comedy Simpsons Sitcom
  • Paintable interfaces
  • Example for multiple select
  • Multiple select applied to interest Information Sports Beverly Hills 90210 Endorsed by Paul Comedy “ Action AND Comedy” Action movies Schwarzen egger Endorsed by Lars M.A.S.H. Basketball Classic music Theater Golf Series Information Beverly Hills 90210 Endorsed by Paul Comedy “ Action AND Comedy” Action movies Schwarzen egger Endorsed by Lars M.A.S.H. Classic music Theater Series
  • Multiple select versus painting
      • Immediate visual feedback allows differentiated input
    Painting Function (tool) selection first, then pixel selection (painting) Multiple select Pixel selection first, then function selection
  • Layout by co-occurrence Danish Milk Pan- cakes Orange Juice Bacon T O T A L French Toast English muffin Hash Browns Ham Eggs Root Beer Milk Shake Cookie Chick Sand Iced Tea Fish sand Fruit Pie Sundae Cheese Burger Ham Burger French Fries Cola Onion Rings Coffee T O T A L
  • A paintable profile editor history B. Hills Soap Comedy M. Arts Action movies Information Schwarz.. Simpsons M.A.S.H. Sports Basket ball C. music Theater Golf Series Series undo save execute Action movies Information Sports B. Hills Soap M.A.S.H. C. music Theater M. Arts Schwarz.. Basket ball Golf Series Series Comedy Simpsons Sitcom Insertion of “sitcom”
  • Paintable time and channel editors
    • Interval sliders are split into segments
    • no handles, just paint the addition
    • Intervals labeled as entities to reduce cluttering
    • Introduction
    • Requirements and related work
    • The TV Scout
      • The TV Scout as a retrieval system…
      • … and as a filtering system
    • How it works
      • The QuerySet Architecture
      • Building QuerySet filtering systems
      • Manual profile editing
    • Conclusions
  • QSA vs. requirements
    • Requirement 1: Exhaustiveness
    • Requirement 2: Output style
    • Requirements 3-5: Adapt to interest changes
    arbitrary interests single ranking User-aggregated relevance feedback Relevance feedback Reuse of old queries (weight set to zero) Gradual change Slowly (caused by process) Repetitive change Abrupt change Rapidly (caused by event) Temporary Permanent
  • Achievements of the dissertation
    • (1) a new generic IF system architecture designed for the efficient handling of highly dynamic interests (the QuerySet Architecture )
    • (2) a new paradigm of high-level access to user profiles ( user-aggregated relevance feedback )
    • (3) a framework of new user interface interaction styles providing users with this high-level access
    • (4) a proof of concept implementation (TV Scout)
  • Future work
    • (1) new application areas
    • (2) new query classes
    • (3) improved aggregation functions
    • (4) new profile editor user interfaces
    • (5) empirical work.
    • END
  • Image processing Luminance Number of pixels there are no black pixels there are no white pixels only rather dark pixels white handle assigns 100% luminance black handle assigns 0% luminance current state of the image desired state of the image gray handle assigns 50% luminance
  • Slide rule ( Rechenschieber ) 1 ½ 0 1 ½ 0 action movies comedies | | merge histograms “ zipper style” c o m e d i e s ¾ ¼ ¾ ¼     a c t i o n m o v i e s
  • Histogram-based interfaces hot! selected rejected Martial arts Legend Comedy shows Entertain- ment Sports 32 out of 333 sports programs per week selected 512 out of 914 movies per week selected Terminator 2 Dead Poets Society Amazons on Mars -------------------------- Le Grand Bleu Back to the Future hot! selected rejected Martial arts Legend 14 out of 14 martial arts programs per week selected Overall: 1094 out of 1797 programs per week selected Save Undo Comedy shows Entertain- ment 536 out of 536 comedy shows per week selected Sports
  • The jelly interface Selected for output Save Undo Auto Overall: 32 out of 59 programs per week selected News Comedy Action
    • STUFF
  • QSA vs. related work
  • QSA can emulate some of them
    • SDI systems (Selected Dissemination of Information
    • Rule-based systems
    • Stereotype-based systems
    • Automated collaborative filtering systems
  • Short break?
  • Chapter 4: User interfaces Normalization and interest intensity editors 1. Form-based 2. Histogram-based 3. and Paintable Interfaces
  • Parameters users know
    • Interest intensities “How important is that query to you”
    • Amounts of objects “How many objects do you want from that query”
  • Relating histograms to each other Moving arrows Moving histograms
  • What is in and what is not? 2. Dead Poets Society 1. Bayern-Manchester 2. Amazons on Mars ------------------------------- 2. Le Grand Bleu 1. Sat1 ran 2. Back to the Future
  • Comparison 2F 1F 0F 2H 1H
  • Results
    • 2D preferred over 1D
    • Computer experts preferred the more powerful histogram-based editors
    • Computer novices prefer form-based
    9 8 7 6 5 4 3 2 1 5 4 3 2 1 0 wonderful horrible horrible wonderful 2F 2H Number of subjects Number of subjects 9 8 7 6 5 4 3 2 1 5 4 3 2 1 0 Computer novice Computer expert
  • Chapter 5: TV Scout
    • TV compared to other application areas
    • TV Scout user interface overview
    • Gathering implicit feedback
    • The TV Scout query classes
  • Properties of TV
    • TV is mainly non-textual
    • TV is broadcast medium
      • no reactions of users, only expectations
      • Annotations date out
      • No incentive for reading new descriptions
    • Broad range of content + interests
    Information source change rate Information need change rate e.g. music movies, e.g. daily news, stock quotes Generic media e.g. TV, radio, Internet task-related (information) taste-related (entertainment)
  • TV vs. other application areas
  • TV Scout User interface
  • Gathering relevance feedback
  • Gathering relevance feedback
    • Profit of relevance feedback
      • Query suggestion, profile optimization
    • For first-time users and casual users this is too far away for being an incentive
    • Use implicit feedback, i.e. monitor user behavior
    • Allows gathering relevance feedback also if users do not plan to have a profile
    • => System can become active and suggest
  • Which implicit feedback to use?
    • examination feedback is ambiguous,
    • reference requires community,
    • … but retention is very natural for planning TV
    [Nichols 97, Oard 98]
  • Implicit retention feedback
    • Monitor retention tools and exact match menu
    • Assign implicit ratings:
      • Default is not inspected
      • Overwrite displayed program descriptions that are not inspected with negative implicit rating not retained
      • Overwrite rating of retained program descriptions with positive implicit rating retained
    • Enhancement
      • Assign negative rating avoided if user queries this date/time/channel segment, but other queries
  • Meaning of retention feedback
    • Implicit retention feedback => all predictions based on that predict what users will retain
      • Retention is not planning to watch
      • Planning to watch is not to watch
      • To watch is not to have liked in the past
    • Retention-based ratings support users only in doing what their task in the TV Scout is—to find the programs that they will want to retain.
  • Query classes of the TV Scout
  • TV Scout query classes
    • Text search
      • FreeWAIS subsystem searches in titles
    • Genres
      • Combined with predicted popularity
      • Extend to automated collaborative filtering
    • Editor’s tips
      • Tips from the professional TV TODAY editors
    • Opinion leaders
      • Users recruited to become “Editors”
  • Query classes: techniques
  • Query classes: applicability
  • Chapter 6: Conclusions
  • Thanks
    • Dieter Boecker
    • Uli Thiel
    • Matthias Hemmje
  • END
  • NOT INCLUDED
  • Classification of IF systems Objects User Rated objects feature extraction matching Profile (= objects) Rated stereotypes stereotype expansion Profile (= stereotypes) Rated attributes Profile (= attributes) feedback attributes
  • Bar chart  histogram Invert f( x )=b -1 ( x ) rating count rank rating Invert b( x )=f -1 ( x ) rating rank Integrate f( x )=-  h( x )d x Differentiate h( x )= - df/d x bar chart histogram
  • Email Profile Editor
  • Channel interface toggle look
  • Banner advertising dialog Daily life Shopping Apparel Food Cosmetics Multimedia Music Games Movies Concerts Books Computer Hardware Software Internet Services Electronics Telecomm. TV Video Hi-fi Mobility Cars Flights Trains Last minute Hotels Money Insurance Stocks Services Contact Jobs Friends Dating Classifieds Sports&Fun Sports Clubs Traveling Infotainment News Magazines Media Competition Free stuff Banking Daily life Shopping Apparel Food Cosmetics Multimedia Music Games Movies Concerts Books Computer Hardware Software Internet Services Electronics Telecomm. TV Video Hi-fi Mobility Cars Flights Trains Last minute Hotels Money Insurance Stocks Services Contact Jobs Friends Dating Classifieds Sports&Fun Sports Clubs Traveling Infotainment News Magazines Media Competition Free stuff Banking done undo done undo
  • Toggle tree maps
  • >> Classification of IF systems Objects User Rated objects feature extraction Profile (= attributes) 1. Feature extraction (Relevance feedback) feedback attributes matching
  • >> Classification of IF systems Objects User feedback Rated attributes attributes Profile (= attributes) 2. Attribute-level interaction (Rules etc.) matching
  • >> Classification of IF systems Objects User Rated stereotypes stereotype expansion Profile (stereotypes) feedback attributes 3. Stereotype expansion matching
  • >> User profiles of related work 4. Automated collaborative filtering Objects User Rated objects compute neighborhood Profile (= objects) feedback attributes matching
  • >> 3. Stereotype-based
    • e.g. GRUNDY [Rich 79]
    • The personality traits that users list to describe themselves are inherently long-term.
    • => no possibility for the users to directly update the representation of their information needs
  • >> 4. Collaborative filtering
    • No aggregation of ratings in user profile
    • Interest change
      • Which ratings have dated out, which are still valid? No way to find this out.
      • => Interest change implies that users have to re-rate large amounts of objects, maybe all
    • This is why ACF is so popular for movies: There are hardly any interest changes
  • IF model [Belkin & Croft 92] d. Inner refinement cycle c. Outer refinement cycle b. Creation Producers of Documents Distributors of Documents Distribution and Representation Regular Information Interest Users/Groups with Long-term goals Representation Comparison or Filtering Modification Use and/or Evaluation Profiles Retrieved Documents Document Surrogates
  • Collaborative filtering
    • Record reactions of users to data objects, e.g. documents ( annotations ) [Goldberg 92]
    • Aggregates annotations and direct them to appropriate recipients.
  • Requirement 4: Output styles 1 n 1 mn 1  weakly ordered output single ranked output “ multiple” ranked outputs
  • Requirement 5: Correctness
    • “King and Queen” example [Mock 96]
      • not interested in the features “King” and “Queen”
      • but are interested in features “Sacramento” and “King” (the basketball team)
    • Linear combination all input features makes conditional independence assumption
      • same values for the word “king”
      • unable to classify these articles correctly
      • => not able to correctly represent multiple unrelated interests
  • Interest changes in literature
    • Gradual changes [Belkin 92, Baclace 91, Lang 95, ...] Consequence of processes, e.g. as people age
      • Example: Favorite TV series
    • Abrupt changes [Marchionini 95, Lam 96, Frisse 89...]
      • Consequence of events
      • Example: Actor quits series
    • Temporary variations [Allen 90, Loeb 92, Kay 95, …]
      • Mood changes
      • Example: In the mood for an action movie
  • How to tackle the problem? Learn from interactive computer graphics
  • Computer graphics vs. Info filtering   Computer graphics Information filtering What is in user’s head… image = assigns color value to image coordinates. relevance function = assigns relevance value to objects. … is modeled as digital image (by Graphics programs) user profile (by IF systems) Sampling-oriented Image = Bitmap images (  ” Painting”) Profile = set of relevance feedback… (  ACF, feature extract.) Object-oriented Image = Set of graphical primitives (  ” Drawing”) Profile = Set of rules etc. (  Rule-based systems)
  • Interactivity in computer graphics
    • IF: Interest changes are not known in advance
    • => CG: interactive animation, e.g. video games
  • Interactivity in computer graphics
  • Interactivity in computer graphics
  • Requirement: Detail and interactivity
    • Requirements
      • high interactivity (rapid reaction to input)
      • graphical quality
    • Video games: scene graph and bitmaps
      • Bitmaps for the details
      • Scene graph for the modifiability
    • Application programs: Assimilate characteristics of other approach
      • Drawing programs => texture maps  [Foley 90].
      • Painting programs => layers [Adobe].
  • Benefit from using layers in CG
    • Creating all layers >= painting a single frame.
    • … but, pays off when the scene is animated
      • Represent change in scene graph (translate, fade in or out, or taint a layer, …)
      • Update only selected layers
    • Group into one layer what will change as a whole
  • Transfer the idea
    • Transfer the idea 2D animation to information filtering
      • n layers => n queries ( Q uery = “a function that assigns ratings to objects” )
      • Scene graph => “Aggregation function”
  • What if overlap is substantial?
    • The WRIR model assumes mutual independence
    • This is not always justified
      • Two queries are used in a data fusion way (=> redundancy)
      • Action, comedy, but user dislikes action comedies (=> implicit interest)
    • => Use model that can learn relation between queries
  • Link matrices          1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 or L probabilities for node to become false probabilities for node to become true w 000 w 001 w 010 w 011 w 100 w 101 w 110 w 111 truth values of the 3 parent nodes
  • w 10 w 00 w 10 w 11 probability of parent node 2 probability of parent node 1 brightness indicates probability of relevance a w 11 (0,0,0,1) + w 00 (1,0,0,0) + w 01 (0,1,0,0) + w 10 (0,0,1,0) (0,¼,¾,1) (0,0,1,1) (1,0,1,0) = (1,1,1,1) (0.6,1,0 .6,0)
  • Model 2: Implementation as inference network r 1 r m d 2 r 3 r 2 q 1 d j I d 1 q n d j-1 … … … QSA profile r 1 r m d 2 r 3 r 2 q 11 d j I 1 d 1 q 21 d j-1 … … … I k q 2n A QSA profile
  • Learning inference network
    • [Baclace 91]
    • Simple “agents” represent each query
    • Complex “agents” represent conjunctions of queries
    • Agents learn from relevance feedback what this query match or combination is worth
  • Normalization: Fitting query rating query rating relevance feedback relevance feedback
  • Normalization in image processing
  • Demo levels dialog d c f e b a
  • Results of user study
    • What confuses users is the surface property “What does the height of these boxes mean?”
    • They had recognized bar charts, not histograms
    • => Better give up bar chart look
    • Which real-world object has the right properties
      • Deformable…
      • …but not compressible (constant volume)
      • Preserves its shape when deformed
  • Histograms help combining knowledge Output ranks Query ranks system needs to compute aggregation function Output ratings histogram set individual histograms Query ratings Objects (if displayed)
  • Inserting queries in QSA profile
  • TV Scout TV Scout interface with starting page viewing time profile editor channel profile editor query menus QSA menu text search program description list program description table suggest queries QSA profile editor QSA profile editor (experts) retention menus video labels laundry list
  • Some design possibilities B. Hills Soap Comedy M. Arts Action movies Information Schwarz.. Simpsons M.A.S.H. Sports Basket ball C. music Theater Golf Series Series Information Sports Beverly Hills 90210 Soap Comedy Martial arts Action movies Schwarzen egger Simpsons M.A.S.H. Basketball Classic music Theater Golf Talk Series Information Sports Beverly Hills 90210 Soap Comedy Martial arts Action movies Schwarzen egger Simpsons M.A.S.H. Basketball Classic music Theater Golf Talk Series Information Sports Beverly Hills 90210 Soap Comedy Martial arts Action movies Schwarzenegger Simpsons M.A.S.H. Basketball Classic music Theater Golf Series
  • Painting (instead of multiple select)
    • Use different colors to express different degrees of like or dislike
    Information Sports Comedy “ Action AND Comedy” Action movies Endorsed by Lars M.A.S.H. Series Basketball Schwarzen egger Beverly Hills 90210 Endorsed by Paul Classic music Theater Golf Information Sports Comedy “ Action AND Comedy” Action movies Endorsed by Lars M.A.S.H. Series Basketball Schwarzen egger “ Action AND Comedy” Action movies Endorsed by Lars M.A.S.H. Basketball Schwarzen egger Basketball Schwarzen egger
  • Semantic space layout
    • Layout according to geographic location of TV stations
  • 3D and 4D paintable interfaces
    • Domains with natural n -dimensional structure
    • Display in n -d
    • Explosion displays keep 2-d painting applicable
  • program descriptions Content provider Query subsystems Exact match filtering Date feedback QSA filtering QSA profile Retention tools Video labels Laundry list ad hoc query Movie database Program description database Time Profile ChannelProfile Time Dialog ChannelDialog Editors’ tips User tips Text search Genres Estim. Pop. ACF
  • Query-executing subsystems
    • Use everything that returns (object, rating) pairs
    • Can use retrieval systems, but also others
    • TV Scout
      • Genres, hand-made function in Sybase database
      • Text searches run in FreeWAIS
      • Editor’s recommendations imported from print magazine
      • User tips done by users
      • Plug in more query-executing subsystems at any time
  • b rating of top-ranked object cut-off output rating query rating  rating of top- ranked object  output rating query rating rating of bottom- ranked object a c amount- defined rating defined output rating query rating 