1. Recommendation system MOPSI project http://cs.uef.fi/mopsi KAROL WAGA 23.04.2013

2. CONTENT CONCEPT OF RECOMMENDATION SYSTEM CURRENT SOURCE OF INFORMATION CONTEXT OF RELEVANCE SYSTEM ARCHITECTURE SCORING SYSTEM EXAMPLE PROPOSED SYSTEM IMPROVEMENTS USER ACTIVITY PHOTOGRAPH CONTENT ANALYSIS 23.04.2013 2

3. 3 CONCEPT – RECOMMENDATION SYSTEM 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 23.04.2013

4. 4 CONCEPT – RECOMMENDATION SYSTEM 23.04.2013

5. CURRENT SOURCE OF INFORMATION SERVICES 5 23.04.2013

6. CURRENT SOURCE OF INFORMATION PHOTOS 6 23.04.2013

7. CURRENT SOURCE OF INFORMATION ROUTES 7 23.04.2013

8. 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) 8 23.04.2013

9. 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 9 23.04.2013

10. CONTEXTS OF RELEVANCE POSITION 10 23.04.2013

11. CONTEXTS OF RELEVANCE INFORMATION 11 23.04.2013

12. CONTEXTS OF RELEVANCE NETWORK 12 23.04.2013

13. CONTEXTS OF RELEVANCE TIME 13 23.04.2013

14. SYSTEM ARCHITECTURE 14 23.04.2013

15. 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 15 23.04.2013

16. 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 and the results from the three groups are merged into one list sorted decreasingly Top items are shown as recommendation to user 16 23.04.2013

17. EXAMPLE 17 23.04.2013

18. EXAMPLE 18 Utra church (262 m) Total score: 3.93 L: 0.97 H: 1.0 R: 0.0 - the nearest service - popular keyword Utra swimming place (575 m) Total score: 3.0 L: 0.90 H: 0.33 R: 0.0 T: 0.88 - 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 23.04.2013

19. PROPOSED SYSTEM IMPROVEMENTS USER ACTIVITY USER PROFILE DETECTING USER ACTIVITY RECORDING USER ACTIVITY CREATING ACTIVITY MODEL PHOTOGRAPH CONTENT ANALYSIS 19 23.04.2013

20. 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. 20 23.04.2013

21. 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. 21 23.04.2013

22. DETECTING USER ACTIVITY (http://www.cs.uef.fi/paikka/karol/doktorat/events%202.4.swf)http://www.cs.uef.fi/paikka/karol/doktorat/events%202.4.swf 22 23.04.2013

23. 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 23 23.04.2013

24. 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 24 23.04.2013

25. Thank you for attention… Any questions? 25 23.04.2013