Recommender Systems 1: Contentbased filtering

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Recommender Systems 1 Content-based filtering
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Why filtering?

• Information overload
• Too many
• movies, books, webpages, songs, plumbers, etc
• Searching is difficult

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Recommender Systems

• Systems that help find the good stuff
• Systems that make personalized recommendations of
  goods, services, and people (Kautz)

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Recommender Systems How

• User identifies one or more objects as being of
  interest
• The recommender system suggests other objects
  that are similar

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What does similar mean?

• Similar in content
• Similar in appreciation by other users gt
  social filtering (next lecture)

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How do we know users opinion?

• Explicit users rate items
• Implicit looking at clicks etc.

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Example XLibris

• User reads text and annotates
• System generates links and further reading list

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Example MovieCentral
To test out a Movie Recommender go to
http//movielens.umn.edu/

• User rates movies
• The system suggests best bets
• Users keep rating movies while checking best bets

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Exercise

• Suppose our user has rated ten movies
• Rating from A to F
• - Jurassic Park - Harry Potter
• - ET - Lord of the Rings
• - Alien - Terminator
• - 101 Dalmatians - Titanic
• - Sleepless in Seattle - Mr Bean
• Which movie do we recommend?

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Observations

• We use our knowledge
• about the items rated
• about other items
• In particular, attributes like type of movie.
• Multiple attributes are likely to be important.

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Needs something like

• Description of items in terms of attributesFor
  example Type, Director, Actors, ...
• Description via keywords
• Possibility to look at content itself, like the
  text

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What has been used

• Important words in content
• 100 words with highest TF-IDF weights (words
  occurring more frequently than on average)
• 128 most informative words (words more associated
  with one class of documents than another)
• For example for restaurant descriptions words
  like noodle, shrimp, basil, exotic,
  salmon

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Example

• noodle shrimp basil exotic
  salmon Opinion
• Kitima Y Y Y Y
  Y -
• MarcoPolo Y Y
  
• Spiga Y Y
  
• Thai Touch Y Y Y
  -
• Dolce Y Y
  Y ?

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Possible algorithm (1)

• Tend to be classifiers
• Learn weights (wi) for words so that ? wi for
  words occurring gt threshold
• Initially, weights are 1
• For each rated example determine sum
• If sum above threshold, and user did not like
  example, then divide all weights by 2

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Possible algorithm (2)

• If sum below threshold, and user did like
  example, then multiply all weights by 2
• Recommend items with highest sum

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Example (Step 1)

• noodle shrimp basil exotic
  salmon
• Weights 1 1 1
  1 1
• Threshold 2

noodle shrimp basil exotic
salmon Opinion Kitima Y Y
Y Y Y - Sum 5 gt 2,
and opinion negative, so, divide weights by 2
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Example (Step 2)

• noodle shrimp basil exotic
  salmon
• Weights 1/2 1/2 1/2 1/2
  1/2
• Threshold 2

noodle shrimp basil exotic
salmon Opinion MarcoPolo Y
Y Sum 1
lt 2, and opinion positive so, multiply by 2
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Example (Step 3)

• noodle shrimp basil exotic
  salmon
• Weights 1/2 1 1 1/2
  1/2
• Threshold 2

noodle shrimp basil exotic
salmon Opinion Spiga Y
Y Sum
1 1/2 lt 2, and opinion positive so, multiply by
2
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Example (Step 4)

• noodle shrimp basil exotic
  salmon
• Weights 1 1 2
  1/2 1/2
• Threshold 2

noodle shrimp basil exotic
salmon Opinion ThaiTouch Y Y
Y - Sum 2
1/2 gt 2, and opinion negative so, divide by 2
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Example (Step 5)

• noodle shrimp basil exotic
  salmon
• Weights 1/2 1/2 2 1/4
  1/2
• Threshold 2

noodle shrimp basil exotic
salmon Opinion ThaiTouch Y Y
Y - Sum 2
1/2 gt 2, and opinion negative so, divide by 2
ETC ETC, repeat till all examples ok, or do all
10 times
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Multimedia Information Retrieval

• Images Photo collections, Face recognition
• Video Movie recommendation, Electronic Program
  Guides
• Spoken documents
• Music
• Other sounds

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Concept-Based Image Retrieval

• Key Concept-based indexing of images
• Based on attributes extracted manually
• Based on logical, high level features
• Systems for image indexing
• ICONCLASS, AAT,
• What?
• Time, location, content

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Content-based Image Retrieval

• Key Automatic indexing of images based on
  low-level features
• Color
• Texture
• Shape
• Spatial orientation and layout
• Sketch

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Examples of Content-Based Image Retrieval

• QBIC - IBMs Query By Image Content
  http//wwwqbic.almaden.ibm.com
• MIT PhotoBook (Source of following examples)
  http//vismod.media.mit.edu/vismod/demos/photoboo
  k/
• VisualSeek http//www.ctr.columbia.edu/VisualSEEk
  

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Image input to search
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Image input to search
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