Using Trust
in Recommender Systems:
  an experimental analysis

            Paolo Massa
         University of Trento

(jo...
Motivation:
1. Recommender Systems recommends
   items the user might like, based on
   past ratings.
2. Now, Decentralize...
Summary
1. Recommender Systems (RSs)
  – Weaknesses
2. Solution: trust-awareness
  – Trust and trust metrics
3. Experiment...
Collaborative Filtering (CF)
1.       Input: ratings given by users to items
     ●     I like “ Titanic” as 4/5
2.       ...
Item 1
                         It e m 2
                                    It e m 3
                                    ...
RSs weaknesses
1. Ratings Matrix sparseness (95-99%)
  – Low or No overlapping (users not comparable)
2. Cold start
  – Ne...
Trust-awareness
1. Trust statement =Rating by human to
   human about her usefulness (ex: in
   providing good movie revie...
Trust Networks




     ME




6 degrees of separation
“ theorem”
Trust metrics
1. Task: based on known trust edges,
   predict trustworthiness of principals
2. Trust propagation (A->B,B->...
Trust solves RS problems
1.   Trust solves CF sparseness problem
   – trust propagation and “ 6 degrees” -> reach many
2. ...
Experiment: Epinions.com
1. Epinions.com' users can
  –   Review and rate items (from 1 to 5)
  –   Keep web of trust (tru...
Epinions' recommendations
Taken one user “ ME” , we can
- use CF on ratings and compute
    “ similarity” of other users
-...
Statistics (1)




#Ratings expressed by Users
(#rev<5) = 52.82%! [Cold start users]
Statistics (2)




#Trust statements expressed by Users
(#trust<5) = 70.18%!
User Similarity Computability
1. Ideally, every user should be
   comparable against every other user.
2. BUT ratings spar...
US computability (cont.)
1. Taken one user, we computed all the
   comparable users.
  – On average an user has 161 compar...
US computability (cont.)




         Cold Start Users


Ex: users with 40 reviews have ~800 comparable users.
           ...
Trust computability
1. Trust metrics predict trust in unknown
   users based on known trust
   statements.
2. Distance fro...
Mean # Reachable Users (in k steps) for users
   expressing X trust statements
    In few steps, you can predict trust in ...
Trust and US computability
                  comparison
  Mean number of Comparable                Mean number of Comparab...
Contribution
Experimental evidence that
  – CF is ineffective in real world scenarios
     •   Especially for Cold Start u...
Future works
1. US and Trust correlate? Contradict?
  – US over trusted is higher than usual?
2. Distrust?
  – Propagation...
Thanks for your attention!



   Questions?

                                    Paolo Massa
                             ...
Collaborative Filtering
Similarity measure: Pearson Correlation
Coefficient of user a and u
                         m
   ...
Hard Trust and Soft Trust
1. Vocabulary:
  – Hard Trust: about security, identity of
    something (user, device, informat...
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Using Trust in Recommender Systems: an experimental analysis

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Using Trust in Recommender Systems: an experimental analysis

  1. 1. Using Trust in Recommender Systems: an experimental analysis Paolo Massa University of Trento (joint work with Bobby Bhattacharjee, UMD)
  2. 2. Motivation: 1. Recommender Systems recommends items the user might like, based on past ratings. 2. Now, Decentralized publishing of info: – Ratings on Items – Trust on Principals [Semantic Web] 3. New issues (sparseness, scalability, trust, attacks, ...) ... Trust-aware Decentralized RS
  3. 3. Summary 1. Recommender Systems (RSs) – Weaknesses 2. Solution: trust-awareness – Trust and trust metrics 3. Experiments on Epinions.com – Evidence trust solves RSs problems – (~50.000 users!) 4. Future works
  4. 4. Collaborative Filtering (CF) 1. Input: ratings given by users to items ● I like “ Titanic” as 4/5 2. I ask recommendation 3. RS computes the similarity of me against every other user ● Pearson correlation coefficient 4. RS find similar users and suggests to me items liked by them.
  5. 5. Item 1 It e m 2 It e m 3 It e m 4 I User1 2 5 ? 5 2 5 5 5 User2 5 1 3 User3 5 5 1 User4 2 2 5 5 5 5 4 4 It does not consider the content of the items, only the ratings given by users. It works independently of the domain (also jokes) BUT Overlapping of rated items required!
  6. 6. RSs weaknesses 1. Ratings Matrix sparseness (95-99%) – Low or No overlapping (users not comparable) 2. Cold start – New users have 0 ratings (->not comparable) 3. Easy Attacks by Malicious Users – Copy profile and become the most similar 4. Hard to understand and control – Black box (bad recs -> user gives up) Solution? Trust of course!
  7. 7. Trust-awareness 1. Trust statement =Rating by human to human about her usefulness (ex: in providing good movie reviews) 2. Explicitly provided 3. Trust is subjective! T(A,Z)=1 & T(B,Z)=0 – No Global BAD principals!!! 4. Trust is asymmetric! I trust Bill Gates. 5. FOAF (Friend-Of-A-Friend) is an XML format to express relationships – Some millions files out there...
  8. 8. Trust Networks ME 6 degrees of separation “ theorem”
  9. 9. Trust metrics 1. Task: based on known trust edges, predict trustworthiness of principals 2. Trust propagation (A->B,B->C|A-?->C) 3. Global (pagerank, ebay, ...) 4. Local (personalized) ME
  10. 10. Trust solves RS problems 1. Trust solves CF sparseness problem – trust propagation and “ 6 degrees” -> reach many 2. Trust solves Cold Start problem – “ just add 1 friend” 3. Trust metrics resistant to copy-profile-attack. – “ you can be similar but if no trust path to you ...” 4. Trust easier to understand and control – trust nets supports Explanation (HCI tests needed) EVIDENCE of 1 and 2 provided by analyzing a REAL, VAST community (Epinions.com)
  11. 11. Experiment: Epinions.com 1. Epinions.com' users can – Review and rate items (from 1 to 5) – Keep web of trust (trust=1) and block list (trust=0). – “ Reviewers whose reviews and ratings you have consistently found to be valuable” (Epinions FAQ) 2. Dataset (by crawling site): – ~50K users, ~140K items, ~660K ratings. – ~500K trust statements. • No block list (not shown on site)
  12. 12. Epinions' recommendations Taken one user “ ME” , we can - use CF on ratings and compute “ similarity” of other users - use Trust Metric and compute “ trustworthiness” of other users Then we can suggest items liked by similar or trustable users. On how many users are they computable?
  13. 13. Statistics (1) #Ratings expressed by Users (#rev<5) = 52.82%! [Cold start users]
  14. 14. Statistics (2) #Trust statements expressed by Users (#trust<5) = 70.18%!
  15. 15. User Similarity Computability 1. Ideally, every user should be comparable against every other user. 2. BUT ratings sparseness = 99.99135% -> tiny overlapping between 2 users 3. Pearson correlation coefficient meaningful only if overlapping(A,U)>1 4. Question: taken one user, how many users are comparable?
  16. 16. US computability (cont.) 1. Taken one user, we computed all the comparable users. – On average an user has 161 comparable users (ideally ~50.000!) 2. We have averaged #comparable_users over users who expressed a certain number of reviews.
  17. 17. US computability (cont.) Cold Start Users Ex: users with 40 reviews have ~800 comparable users. BUT users (y axis) are ~50.000! And for Cold Start Users (>50%) this is 2.74
  18. 18. Trust computability 1. Trust metrics predict trust in unknown users based on known trust statements. 2. Distance from ME to U is a first measure of Trust computability 3. On average, – In 2 steps, reach 400 users – In 3 steps, reach 4386 users
  19. 19. Mean # Reachable Users (in k steps) for users expressing X trust statements In few steps, you can predict trust in every user! Even for Cold Start Users!!!
  20. 20. Trust and US computability comparison Mean number of Comparable Mean number of Comparable users for All users users for Cold Start users Propagating Trust Using Propagating Trust Using Dist 1 Dist 2 Dist 3 Dist 4 Pearson Dist 1 Dist 2 Dist 3 Dist 4 Pearson 9.88 400 4386 16334 161 2.14 94.54 1675 9121 2.74
  21. 21. Contribution Experimental evidence that – CF is ineffective in real world scenarios • Especially for Cold Start users. – Trust can solve CF problems • Sparseness • Cold Start • Attacks (self-evident) Trust is computable on many more users than user similarity Especially for cold start users (the majority!)
  22. 22. Future works 1. US and Trust correlate? Contradict? – US over trusted is higher than usual? 2. Distrust? – Propagation? Properties? 3. Design a Trust Metric (for RS) – Create and evaluate a Trust-aware RS • Input data
  23. 23. Thanks for your attention! Questions? Paolo Massa Email: massa@itc.it Blog: http://moloko.itc.it/paoloblog/index.html
  24. 24. Collaborative Filtering Similarity measure: Pearson Correlation Coefficient of user a and u m ∑i=1 r a ,i −r a r u ,i −r u  w a , u= ∑ m i=1 r a , i −r u  2 m ∑i=1 r u , i −r u  2 Prediction of rating given by user a to ite n ∑u=1  r u , i −ru ∗w a , u p a , i =r a   n ∑u=1 w a , u
  25. 25. Hard Trust and Soft Trust 1. Vocabulary: – Hard Trust: about security, identity of something (user, device, information) • Public key cryptography – Soft Trust: appreciation of some principal (explicitly provided by another principal) • Social Networks and Trust Metrics
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