Transcript Recommendation system

CONTENT
• CONCEPT OF RECOMMENDATION SYSTEM
•
•
•
•
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CURRENT SOURCE OF INFORMATION
CONTEXT OF RELEVANCE
SYSTEM ARCHITECTURE
SCORING SYSTEM
EXAMPLE
• PROPOSED SYSTEM IMPROVEMENTS
• USER ACTIVITY
• PHOTOGRAPH CONTENT ANALYSIS
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CONCEPT – RECOMMENDATION SYSTEM
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is a subclass of information filtering system that seek to predict the 'rating' or
'preference' that user would give to an item (such as music, books, or movies) or
social element (e.g. people or groups) they had not yet considered, using a model
built from the characteristics of an item (content-based approaches) or the user's
social environment (collaborative filtering approaches).
BENEFITS OF THE RECOMMENDATION SYSTEM:
1. finding items relevant to user among many items
2. personalized based on real activity
3. allow discovering things similar to what one already
liked
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CONCEPT – RECOMMENDATION SYSTEM
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CURRENT SOURCE OF INFORMATION
SERVICES
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CURRENT SOURCE OF INFORMATION
PHOTOS
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CURRENT SOURCE OF INFORMATION
ROUTES
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CONTEXTS OF RELEVANCE
P - Position (what is nearby)
I - Information (filter relevant information)
N - Network (what others have looked for and found
useful)
T - Time (what is useful now)
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CONTEXTS OF RELEVANCE
P – if user is in Science Park lunch restaurants in
Käpykangas are not relevant
I – if user does not like sports then nearby gyms, jogging
tracks, skiing tracks are not important for him
N – restaurant rated well by users should be
recommended even if it's further than restaurants
without user rating
T – in summer time skiing tracks and skating rinks are
not relevant
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CONTEXTS OF RELEVANCE
POSITION
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CONTEXTS OF RELEVANCE
INFORMATION
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CONTEXTS OF RELEVANCE
NETWORK
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CONTEXTS OF RELEVANCE
TIME
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SYSTEM ARCHITECTURE
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THE SCORING SYSTEM
• Items for scoring are selected based on distance from
user’s location
• Services are scored based on position, search history
and rating. As ”high quality” source services are
promoted by adding 1 to their score (instead of time
scoring that is applied to photos and routes)
• Photos are scored based on position, search history
and rating and time
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THE SCORING SYSTEM
• Routes are scored based on position, attractivity
(number of services and pictures in the end point and
along the route) and time
• Scores are normalized to <0,1> and the results from
the three groups are merged into one list sorted
decreasingly
• Top items are shown as recommendation to user
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EXAMPLE
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EXAMPLE
Utra swimming place (575 m)
Total score: 3.0
L: 0.90 H: 0.33 R: 0.0 T: 0.88
Utra church (262 m)
Total score: 3.93
L: 0.97 H: 1.0 R: 0.0
- the nearest service
- popular keyword
- nearby photo
- taken in the same season of the year
Utrantie 88 – Kalevankatu 29 (34 m)
Total score: 3.1
L: 1.0 A: 1.0 T: 0.1
- the nearest item in database
- leads to attractive destination with many pictures
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PROPOSED SYSTEM IMPROVEMENTS
• USER ACTIVITY
• USER PROFILE
• DETECTING USER ACTIVITY
• RECORDING USER ACTIVITY
• CREATING ACTIVITY MODEL
• PHOTOGRAPH CONTENT ANALYSIS
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USER PROFILE
•
is the computer representation of a user model.
The main goal of user modeling is customization and
adaptation of systems to the user's specific needs.
Gathering information about user is done by recording
user activity on website and in mobile application,
detecting user activities in the real world and analysing
user's collection.
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RECORDING USER ACTIVITY
1) Storing activities on client side in web browser
(Javascript) and on mobile devices
2) Sending data to server (JSON)
3) Parsing data and saving to database (PHP and
MySQL)
All the stages are based on activity model.
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DETECTING USER ACTIVITY
(http://www.cs.uef.fi/paikka/karol/doktorat/events%202.4.swf)
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CONTENT of user profile
List of favorite keywords based on rating (services and
photos) and visits (services) to recommend items with
these keywords with higher probability – involved
keyword clustering
List of services and photos rated bad to avoid
recommending these items
Movement type statistics to recommend favorite type of
routes
Similarity matrix with other users based on similarity of
favorite keywords, route types and number of common
friends (Facebook), detected meeting number
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PHOTOGRAPH CONTENT ANALYSIS
INPUT: a MOPSI photo
• retrieve pictures from Flickr in the same location
• use open source project for image similarity
• use perceptual hash to sort output based on similarity
• get tags from Flickr of the most similar images
OUTPUT: set of keywords describing the MOPSI photo
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Thank you for attention…
Any questions?
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