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Introduction   Recommender systems   Related work      A recommender system for Twitter   Experiments   Conclusions




                 Towards trust-aware recommender systems

                                          Alberto Lumbreras
                                       Ricard Gavaldà (Advisor)




                                                    July 2012
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction      Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                                Introduction




               The paradox of choice: "more is less"
               Recommender systems to reduce
               information overload
               Social networks information
               to enhance recommendations
Introduction      Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                          Aims of the Thesis



                                                                           Recommend tweets
                                                                       to users based on their
                                                                    social network information




               Studying the concept of trust in Twitter
               Can trust improve tweet recommendations?
               What other techniques are useful?
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction      Recommender systems     Related work   A recommender system for Twitter   Experiments   Conclusions



                                        Recommender systems


               Collaborative Filtering:
                 • Look at "similar users" (similar ratings)
                 • Average their ratings weighted by closeness.

                 • Sparsity
                 • Cold-start problem


               Content Based:
                 • Based on items similarity / machine learning
                 • Features extracted automatically or by experts

                 • Cold-start problem
                 • What features are relevant?


               Trust-aware:
                 • Use trust to enhance recommendation methods

                 • What is trust?
                 • How to compute and propagate trust?
Introduction      Recommender systems    Related work    A recommender system for Twitter   Experiments   Conclusions



                                             Computing Trust



               Direct trust computation:
                 • Explicit: Users annotations
                 • Implicit: Inferred from users’ behavior and/or interactions



               Trust propagation:
                 • Algorithms fit network properties (decay, trust horizon,...)
                 • Network as Markov Chain: PageRank, EigenTrust...
                 • ...



               Trust-aware recommendations:
                 • Trust + Collaborative Filtering
                 • Trust + Content Based Filtering
                 • ...
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction      Recommender systems     Related work    A recommender system for Twitter   Experiments   Conclusions



                                                  Related work



               TidalTrust:
                 • Movie recommendation.
                 • Customized algorithm for trust propagation
                 • Direct trust explicitly annotated by users (0-10 range)

                 • Pro: Do not normalize trust
                 • Con: Requires annotated direct trust


               EigenTrust:
                 •   Reputation in P2P networks
                 •   Direct trust inferred from proportion of successful downloads
                 •   Trust propagation by Random Walk
                 •   Distributed computation
                 • Pro: Simple algorithm
                 • Con: Do not explicitly consider network properties
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction      Recommender systems     Related work     A recommender system for Twitter   Experiments   Conclusions



                                          Goals and challenges



                                                         GOAL:

               Recommend tweets to users based on their social network information


                                               CHALLENGES:
               In GETTING the data:
                 • APIs limits
                 • Implement a crawler
                 • Crawling criteria


               In ANALYZING the data:
                 •   No explicit feedback mechanism
                 •   Textual items of 140 characters
                 •   Items volatility (hours or days)
                 •   Very high sparsity (items rated (retweets) by few users)
Introduction      Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                               Contributions




               Trust metric (for social networks)
               Trust-aware crawler (for social networks)
               Recommender system prototype
               Analysis of trust properties in Twitter
Introduction      Recommender systems     Related work   A recommender system for Twitter   Experiments   Conclusions



                                        Computing trust in Twitter
                                              Interactions in Twitter



               tweet:
                 • @xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r

               mention:
                 • @bob: Nice tutorial @xamat!
                 • @bob: Not a very good tutorial @xamat...
                 • trust or distrust (assumption: mostly trust)

               retweet (forward) :
                 • @charles: RT "@xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r"
                 • @charles: So bad! RT "@xamat: Markov Random Fields for #recsys:
                    http://t.co/zHggOl2r"
                 • trust or distrust (observation: mostly trust)

               favorite:
                 • store a friend’s tweet
                 • observation: mostly trust

               follow/unfollow :
                 • users subscribe/unsubscribe to other users publications
Introduction      Recommender systems     Related work         A recommender system for Twitter   Experiments    Conclusions



                                        Computing trust in Twitter
                                                         Direct trust




               Mentions and retweets as a sign of trust
               Trust tij as proportion of interactions of user i with user j.
                                                         (m)                   (rt)
                                                   wNij        + (1 − w )Nij
                                           tij =                                                                (1)
                                                                 Ni

               Temporal decay of interactions:
                                        Ni (t) = λNi (t) + (1 − λ)Ni (t − 1)                                    (2)
Introduction      Recommender systems        Related work    A recommender system for Twitter   Experiments   Conclusions



                                         Computing trust in Twitter
                                        Trust propagation through random walk


               Step 1: With direct trusts, build transition probabilities matrix P




                                                                              
                                                0           0.2    0.8      0
                                              0             0     0.5     0.5 
                                           P=
                                              0
                                                                               
                                                             0      0       1 
                                               0.3          0.3    0.3      0
Introduction      Recommender systems        Related work           A recommender system for Twitter       Experiments    Conclusions



                                         Computing trust in Twitter
                                        Trust propagation through random walk




               Step 2: Propagate trust
                                         1
                              T3 =         (α1       P         +α2        P2        +α3        P3      )                 (3)
                                         3
                                                 walk 1 step         walk 2 steps         walk 3 steps

                                                                      s
                                                                1
                                                      Ts =                αn P n                                         (4)
                                                                s   n=1

        P: direct trust matrix (transition matrix)
        s: trust horizon
        αn : path length penalization αn
               Note: Assumes transitivity
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                             Architecture
Introduction   Recommender systems   Related work      A recommender system for Twitter   Experiments   Conclusions



                                                    Crawler




        Algorithm 1: Crawling cycle
        while True do
            S ←− SeedUsers() ∪ TopTrustedUsers()
            foreach s ∈ S do
                statuses ←− GetLastUpdates(s)
                UpdateInteracctionsMatrix(s, statuses)
            end
            TruncateInteractionsMatrix() /*remove unsignificant users*/
            UpdateTrustMatrix()
            UpdateTopTrustedUsers()
        end
Introduction      Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                              Recommender




               Optional: Tweet expansion.
               Optional: bag-of-words or tfPOS /tfNEG ratio.
               Tweet candidates from top-trusted neighborhood
               Learn to predict retweets.
Introduction     Recommender systems   Related work    A recommender system for Twitter   Experiments   Conclusions



                                             Recommender
                                          Query (tweet) expansion




               Query expansion:
                 • Bag of words probably not enough. Too few word coincidences between tweets
                 • If expanding the query (i.e: synonyms) more chances to get coincidences
                 • Query expansion proved useful in some scenarios (i.e: for QA systems with search
                   engines)

               Tweet expansion:
                 • Query Bing with tweets
                 • Get first 200 results (summaries)
                 • Add summaries words to tweet
Introduction      Recommender systems       Related work   A recommender system for Twitter   Experiments   Conclusions



                                                  Recommender
                                        Scoring: Trust-aware + Content-based




               Features:
                 • has_url [True, False]
                 • bag-of-words or tf ratio
                 • trust [0,1]
               Label:
                 • retweet [True, False]
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction      Recommender systems   Related work      A recommender system for Twitter   Experiments   Conclusions



                                                       Dataset




               20 target users
               6 month crawling
               Spanish, Catalan, English
               314 instances/user (50% retweets, 50% non-retweets)
               70% training, 30% test.
               Offline testing (a posteriori prediction of retweets)
Introduction      Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                            Transitivity tests


               Normalize trust rankings [0-10]
               ∆: Disagreement about ranking of common neighbors.




               If transitivity: the higher trust on a user, the smaller ∆ between their ratings
Introduction      Recommender systems         Related work        A recommender system for Twitter   Experiments   Conclusions



                                                  Transitivity tests
                                                             Interactions


               Question: are interactions of Twitter users transitive?


                                        Table: Relation of interactions rank and delta

                                                  Ranking [0-10]             ∆
                                                     [0 − 1)                1.36
                                                     [1 − 2)                0.75
                                                     [2 − 3)                1.14
                                                     [3 − 4)                0.83
                                                     [4 − 5)                0.78
                                                     [5 − 6)                0.52
                                                     [6 − 7)                1.06
                                                     [7 − 8)                0.53
                                                     [8 − 9)                0.57
                                                    [9 − 10)                0.17

               Agreement about common neighbors (no matter the ∆)
               ... but we do not see transitivity.
Introduction      Recommender systems     Related work    A recommender system for Twitter   Experiments   Conclusions



                                              Transitivity tests
                                                         Trust


               different path decays (αn )


                                        Table: Relation of trust rank and delta

                            Ranking         ∆                 ∆                    ∆
                            (0-10)       No decay        Linear decay           Exp.decay
                             (0-1)         1.14              0.90                 0.87
                             (1-2)         1.10              1.22                 1.09
                             (2-3)         0.96              1.05                 1.05
                             (3-4)         1.07              1.18                 1.20
                             (4-5)         1.09              0.81                 1.01
                             (5-6)         1.13              1.11                 0.92
                             (6-7)         0.91              1.08                 1.16
                             (7-8)         0.90              1.08                 0.99
                             (8-9)          1.3              1.34                 1.34
                            (9-10)         1.26              1.11                 1.16

               we do not see transitivity
Introduction             Recommender systems   Related work      A recommender system for Twitter   Experiments       Conclusions



                                           Trust-aware recommendations
                                                         Benchmarking
                      trust slightly improves recommendations

                       Classif.      Expand    Encode         Acc.      Recall        Precision      F1           AUC
                                                bow           50.76     59.43          52.07        55.51         50.93
                                       yes
                                                 tf           52.38     58.99          52.52        55.57         52.58
                         NB
                                                bow           48.79     53.01          47.76        50.25         49.88
                                        no
           Trust




                                                 tf           51.97     50.49          50.60        50.44         51.51
                                                bow           45.84     61.00          30.22        40.42         50.53
                                       yes
                                                 tf           47.63     51.36          46.95        49.06         47.94
                        SVM
                                                bow           46.25     68.42          32.47        44.04         50.00
                                        no
                                                 tf           47.79     46.38          48.44        47.39         47.32
                                  Averages                    48.93     56.13          45.13        49.08         50.09
                                                bow           47.02     57.58          48.90        52.89         49.56
                                       yes
                                                 tf           51.13     49.56          54.81        52.05         52.22
                         NB
                                                bow           49.28     47.98          50.76        49.33         50.54
           No trust




                                        no
                                                 tf           46.12     45.18          45.84        45.51         46.28
                                                bow           45.22     55.83          27.05        36.44         50.06
                                       yes
                                                 tf           49.23     49.36          51.47        59.39         49.80
                        SVM
                                                bow           43.40     34.87          17.59        23.38         50.22
                                        no
                                                 tf           47.11     41.55          48.87        44.91         47.32
                                  Averages                    47.31     47.73          43.16        45.49         49.50
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




        1. Introduction


        2. Recommender systems


        3. Related work


        4. A recommender system for Twitter


        5. Experiments


        6. Conclusions
Introduction      Recommender systems     Related work   A recommender system for Twitter   Experiments   Conclusions



                                        Contributions and findings



        Contributions:
               Trust metric
               Trust-aware crawler for social networks
               Recommender system prototype
               Analysis of trust properties in Twitter




        Findings:
               No transitivity of trust in Twitter or bad trust model...
               ...but trust model might be useful
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                              Publication




        A. Lumbreras, R. Gavaldà, “Applying trust metrics based on user interactions to
        recommendation in social networks” in Social Knowledge Discovery and Utilization
        Workshop within IEEE/ACM ASONAM’2012, Istambul, August 2012.
Introduction      Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions



                                                 Future work




               Other query expansion techniques
               Further text analysis (e.g: LSA)
               Apply temporal decay to tweets
               Further study of network properties (trust, interactions, visualization...)
               User tests
               Study marginal contribution of retweets and mentions
               Topic-aware trust (topic detection)
               Open question: (how much) transitivity-based models can capture trust on a
               non-transitive trust network?
Introduction   Recommender systems   Related work   A recommender system for Twitter   Experiments   Conclusions




                                     Questions?

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Towards trust-aware recommender systems

  • 1. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Towards trust-aware recommender systems Alberto Lumbreras Ricard Gavaldà (Advisor) July 2012
  • 2. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 3. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 4. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Introduction The paradox of choice: "more is less" Recommender systems to reduce information overload Social networks information to enhance recommendations
  • 5. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Aims of the Thesis Recommend tweets to users based on their social network information Studying the concept of trust in Twitter Can trust improve tweet recommendations? What other techniques are useful?
  • 6. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 7. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Recommender systems Collaborative Filtering: • Look at "similar users" (similar ratings) • Average their ratings weighted by closeness. • Sparsity • Cold-start problem Content Based: • Based on items similarity / machine learning • Features extracted automatically or by experts • Cold-start problem • What features are relevant? Trust-aware: • Use trust to enhance recommendation methods • What is trust? • How to compute and propagate trust?
  • 8. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Computing Trust Direct trust computation: • Explicit: Users annotations • Implicit: Inferred from users’ behavior and/or interactions Trust propagation: • Algorithms fit network properties (decay, trust horizon,...) • Network as Markov Chain: PageRank, EigenTrust... • ... Trust-aware recommendations: • Trust + Collaborative Filtering • Trust + Content Based Filtering • ...
  • 9. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 10. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Related work TidalTrust: • Movie recommendation. • Customized algorithm for trust propagation • Direct trust explicitly annotated by users (0-10 range) • Pro: Do not normalize trust • Con: Requires annotated direct trust EigenTrust: • Reputation in P2P networks • Direct trust inferred from proportion of successful downloads • Trust propagation by Random Walk • Distributed computation • Pro: Simple algorithm • Con: Do not explicitly consider network properties
  • 11. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 12. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Goals and challenges GOAL: Recommend tweets to users based on their social network information CHALLENGES: In GETTING the data: • APIs limits • Implement a crawler • Crawling criteria In ANALYZING the data: • No explicit feedback mechanism • Textual items of 140 characters • Items volatility (hours or days) • Very high sparsity (items rated (retweets) by few users)
  • 13. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Contributions Trust metric (for social networks) Trust-aware crawler (for social networks) Recommender system prototype Analysis of trust properties in Twitter
  • 14. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Computing trust in Twitter Interactions in Twitter tweet: • @xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r mention: • @bob: Nice tutorial @xamat! • @bob: Not a very good tutorial @xamat... • trust or distrust (assumption: mostly trust) retweet (forward) : • @charles: RT "@xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r" • @charles: So bad! RT "@xamat: Markov Random Fields for #recsys: http://t.co/zHggOl2r" • trust or distrust (observation: mostly trust) favorite: • store a friend’s tweet • observation: mostly trust follow/unfollow : • users subscribe/unsubscribe to other users publications
  • 15. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Computing trust in Twitter Direct trust Mentions and retweets as a sign of trust Trust tij as proportion of interactions of user i with user j. (m) (rt) wNij + (1 − w )Nij tij = (1) Ni Temporal decay of interactions: Ni (t) = λNi (t) + (1 − λ)Ni (t − 1) (2)
  • 16. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Computing trust in Twitter Trust propagation through random walk Step 1: With direct trusts, build transition probabilities matrix P   0 0.2 0.8 0  0 0 0.5 0.5  P=  0  0 0 1  0.3 0.3 0.3 0
  • 17. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Computing trust in Twitter Trust propagation through random walk Step 2: Propagate trust 1 T3 = (α1 P +α2 P2 +α3 P3 ) (3) 3 walk 1 step walk 2 steps walk 3 steps s 1 Ts = αn P n (4) s n=1 P: direct trust matrix (transition matrix) s: trust horizon αn : path length penalization αn Note: Assumes transitivity
  • 18. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Architecture
  • 19. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Crawler Algorithm 1: Crawling cycle while True do S ←− SeedUsers() ∪ TopTrustedUsers() foreach s ∈ S do statuses ←− GetLastUpdates(s) UpdateInteracctionsMatrix(s, statuses) end TruncateInteractionsMatrix() /*remove unsignificant users*/ UpdateTrustMatrix() UpdateTopTrustedUsers() end
  • 20. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Recommender Optional: Tweet expansion. Optional: bag-of-words or tfPOS /tfNEG ratio. Tweet candidates from top-trusted neighborhood Learn to predict retweets.
  • 21. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Recommender Query (tweet) expansion Query expansion: • Bag of words probably not enough. Too few word coincidences between tweets • If expanding the query (i.e: synonyms) more chances to get coincidences • Query expansion proved useful in some scenarios (i.e: for QA systems with search engines) Tweet expansion: • Query Bing with tweets • Get first 200 results (summaries) • Add summaries words to tweet
  • 22. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Recommender Scoring: Trust-aware + Content-based Features: • has_url [True, False] • bag-of-words or tf ratio • trust [0,1] Label: • retweet [True, False]
  • 23. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 24. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Dataset 20 target users 6 month crawling Spanish, Catalan, English 314 instances/user (50% retweets, 50% non-retweets) 70% training, 30% test. Offline testing (a posteriori prediction of retweets)
  • 25. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Transitivity tests Normalize trust rankings [0-10] ∆: Disagreement about ranking of common neighbors. If transitivity: the higher trust on a user, the smaller ∆ between their ratings
  • 26. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Transitivity tests Interactions Question: are interactions of Twitter users transitive? Table: Relation of interactions rank and delta Ranking [0-10] ∆ [0 − 1) 1.36 [1 − 2) 0.75 [2 − 3) 1.14 [3 − 4) 0.83 [4 − 5) 0.78 [5 − 6) 0.52 [6 − 7) 1.06 [7 − 8) 0.53 [8 − 9) 0.57 [9 − 10) 0.17 Agreement about common neighbors (no matter the ∆) ... but we do not see transitivity.
  • 27. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Transitivity tests Trust different path decays (αn ) Table: Relation of trust rank and delta Ranking ∆ ∆ ∆ (0-10) No decay Linear decay Exp.decay (0-1) 1.14 0.90 0.87 (1-2) 1.10 1.22 1.09 (2-3) 0.96 1.05 1.05 (3-4) 1.07 1.18 1.20 (4-5) 1.09 0.81 1.01 (5-6) 1.13 1.11 0.92 (6-7) 0.91 1.08 1.16 (7-8) 0.90 1.08 0.99 (8-9) 1.3 1.34 1.34 (9-10) 1.26 1.11 1.16 we do not see transitivity
  • 28. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Trust-aware recommendations Benchmarking trust slightly improves recommendations Classif. Expand Encode Acc. Recall Precision F1 AUC bow 50.76 59.43 52.07 55.51 50.93 yes tf 52.38 58.99 52.52 55.57 52.58 NB bow 48.79 53.01 47.76 50.25 49.88 no Trust tf 51.97 50.49 50.60 50.44 51.51 bow 45.84 61.00 30.22 40.42 50.53 yes tf 47.63 51.36 46.95 49.06 47.94 SVM bow 46.25 68.42 32.47 44.04 50.00 no tf 47.79 46.38 48.44 47.39 47.32 Averages 48.93 56.13 45.13 49.08 50.09 bow 47.02 57.58 48.90 52.89 49.56 yes tf 51.13 49.56 54.81 52.05 52.22 NB bow 49.28 47.98 50.76 49.33 50.54 No trust no tf 46.12 45.18 45.84 45.51 46.28 bow 45.22 55.83 27.05 36.44 50.06 yes tf 49.23 49.36 51.47 59.39 49.80 SVM bow 43.40 34.87 17.59 23.38 50.22 no tf 47.11 41.55 48.87 44.91 47.32 Averages 47.31 47.73 43.16 45.49 49.50
  • 29. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions 1. Introduction 2. Recommender systems 3. Related work 4. A recommender system for Twitter 5. Experiments 6. Conclusions
  • 30. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Contributions and findings Contributions: Trust metric Trust-aware crawler for social networks Recommender system prototype Analysis of trust properties in Twitter Findings: No transitivity of trust in Twitter or bad trust model... ...but trust model might be useful
  • 31. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Publication A. Lumbreras, R. Gavaldà, “Applying trust metrics based on user interactions to recommendation in social networks” in Social Knowledge Discovery and Utilization Workshop within IEEE/ACM ASONAM’2012, Istambul, August 2012.
  • 32. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Future work Other query expansion techniques Further text analysis (e.g: LSA) Apply temporal decay to tweets Further study of network properties (trust, interactions, visualization...) User tests Study marginal contribution of retweets and mentions Topic-aware trust (topic detection) Open question: (how much) transitivity-based models can capture trust on a non-transitive trust network?
  • 33. Introduction Recommender systems Related work A recommender system for Twitter Experiments Conclusions Questions?