Paolo Cremonesi




IPTV Recommender Systems
2
Agenda

•   IPTV architecture
•   Recommender algorithms
•   Evaluation of different algorithms
•   Multi-model systems
...
3
Valentino Rossi




      Paolo Cremonesi - Recommender Systems
4
   IPTV architecture




                     Live TV

                                                            Set-t...
5
IPTV architecture

•   IPTV is a video service supplied by a telecom service provider that
    owns the network infrastr...
6
IPTV Platform: Now



                                                  CUSTOMERS FACE
                                 ...
7
IPTV Platform: with a recommender systems




     From this….




                              Today recommendations,
...
8
IPTV recommender needs


•   Improve user satisfaction

•   Sell new content to users
        VOD
        Pay-per-view c...
9
Agenda

•   IPTV architecture
•   Recommender algorithms
•   Evaluation of different algorithms
•   Multi-model systems
...
10
Recommender System: how it works


                                 USER’S TASTE
          USER                        ...
11
    Problem formulation




Users ratings                                                       Items metadata
        ...
12
Recommendation techniques

                                  Recommender
                                   algorithms
...
13
Memory vs. model based

                                     Memory      Model
                                      ba...
14
    Collaborative Filtering



                                                    User-based
                         ...
15
Collaborative filtering: User Rating Matrix




                User




                                  Item




   ...
16
User rating matrix URM




     I1      I2      I3     I4                         I1   I2   I3   I4

U1   3        4   ...
17
Dimensional-reduction collaborative model

• items and users can be described by a number (K) of
  unknown features
• a...
18
Singular Value Decomposition


mxn



      R
      A                =          U              S         VTT
          ...
19
Singular Value Decomposition


                                        T
R=U·S·V
             T
V·V =I
   T
U ·U=I



 ...
20
 Singular Value Decomposition


                                                 T
R k = U k · Sk ·                    ...
21
Folding-in

•   New rows/columns of A are projected (folded-in) in the existing latent
    space without computing a ne...
22
Collaborative Filtering: pro & cons

•   Pro:
       There is no need for content

•   Cons:
       Cold Start: we need...
23
 Content-based Filtering




                                                               Term 3
      ...mettendo a ...
24
Content-based filtering: Item-Content Matrix




                Word




                                             ...
25
Content-based Filtering: techniques




                                  Term 3
     User-item
     similarity




   ...
26
Content-based Filtering: pro & cons

•   Pro:
       No need for data on other users
       No cold-start or sparsity p...
27
    Content-based Filtering: Latent Semantic Analysis



            svd                                  Sk           ...
28
      Recommender architecture



Resources management

                            Features         Features
  Items  ...
29
Datasets


                           Real datasets composed by movies and user
                           fruitions, p...
30



•   Implicit vs Explicit
•   Come determinare il rating implicito
       VOD
       TV (EPG)




        Paolo Cremo...
31
Some problems with IPTV recommender

•   Cold start

•   Multi-language content
       (e.g., Switzerland)

•   New use...
32
Agenda

•   IPTV architecture
•   Recommender algorithms
•   Evaluation of different algorithms
•   Multi-model systems...
33
Problem

•   Many works do not describe clearly the methods used for
    performance evaluation and model comparison
• ...
34
Objective

•   Design a new methodology to compare different algorithms according
    to                               ...
35
Metrics

• Error metrics                                • Accuracy metrics
      Mean Square Error                     ...
36
Accuracy metrics




      Paolo Cremonesi - Recommender Systems
37
Accuracy metrics




      Paolo Cremonesi - Recommender Systems
38
Accuracy metrics




      Paolo Cremonesi - Recommender Systems
39
Netflix dataset: test user profile




       Paolo Cremonesi - Recommender Systems
40
Netflix dataset: Global effects algorithm




                                              RMSE: 0.95
                ...
41
Netflix dataset: Adjusted cosine algorithm




                                              RMSE: 1.6
                ...
42
Netflix dataset: SVD algorithm




                                              RMSE: 2.7
                            ...
43
Quality evaluation

• Focus on future performance on new data
• Proper partitioning of original data set into:         ...
44
Hold-out




      Paolo Cremonesi - Recommender Systems
45
Leave-one-out




      Paolo Cremonesi - Recommender Systems
46
K-fold




         Paolo Cremonesi - Recommender Systems
47
Agenda

•   IPTV architecture
•   Recommender algorithms
•   Evaluation of different algorithms
•   Multi-model systems...
48
   Recommender system architecture




   Inputs                                                                       ...
Proposed approach

•   Batch system

       Statistical analysis of the dataset
       Definition of a number of models
  ...
Multi-model recommender engine




     Paolo Cremonesi - Recommender Systems
51
Dataset statistical analysis (example)




      Paolo Cremonesi - Recommender Systems
52
Dataset statistical analysis (example)


                                                1
 Percentage of rated items i...
53
Dataset statistical analysis (example)



    User groups                                Item popularity



  20 or mor...
54
Popular vs. unpopular: SVD algorithm - NF



             0.35
                                                        ...
55
Popular vs. unpopular: SVD algorithm - NM




                                                            all
         ...
56
    User profile length – NM recall
             Group           SVD          Cos -like      NBN_S    NBN_I    NBN_U
  ...
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IPTV Recommender Systems

  1. 1. Paolo Cremonesi IPTV Recommender Systems
  2. 2. 2 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
  3. 3. 3 Valentino Rossi Paolo Cremonesi - Recommender Systems
  4. 4. 4 IPTV architecture Live TV Set-top-box (decoder) VOD Content Provider Service Provider Network Provider Customers Paolo Cremonesi - Recommender Systems
  5. 5. 5 IPTV architecture • IPTV is a video service supplied by a telecom service provider that owns the network infrastructure and controls content distribution over the broadband network for reliable delivery to the consumer (generally to the TV/IP STB). • Services Broadcast TV (BTV) services which consist in the simultaneous reception by the users of a traditional TV channel, Free-to-air or Pay TV. BTV services are usually implemented using IP multicast protocols. Video On Demand (VOD) services, which consist in viewing multimedia contents made available by the Service Provider, upon request. VOD services are usually implemented using IP unicast protocols. Paolo Cremonesi - Recommender Systems
  6. 6. 6 IPTV Platform: Now CUSTOMERS FACE DIFFICULTIES FINDING HUNDREDS THE “RIGHT” CONTENT LIVE CHANNELS THOUSANDS CUSTOMER VOD PURCHASES ITEMS CUSTOMER FRUSTRATION Paolo Cremonesi - Recommender Systems
  7. 7. 7 IPTV Platform: with a recommender systems From this…. Today recommendations, based on your personal taste, are: To this. Paolo Cremonesi - Recommender Systems
  8. 8. 8 IPTV recommender needs • Improve user satisfaction • Sell new content to users VOD Pay-per-view channels • Targeting advertisement Paolo Cremonesi - Recommender Systems
  9. 9. 9 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
  10. 10. 10 Recommender System: how it works USER’S TASTE USER CONTENT DATA FRUTIONS AND METADATA RATINGS RECOMMENDER SYSTEM CONTENT RECOMMENDATIONS Paolo Cremonesi - Recommender Systems
  11. 11. 11 Problem formulation Users ratings Items metadata Recommender Ranked list •Item1 •Item2 Top N •Item3 •. •. •. •ItemX Paolo Cremonesi - Recommender Systems
  12. 12. 12 Recommendation techniques Recommender algorithms Collaborative Filtering User Item Content-based based based Filtering Users with similar taste Similar Items Paolo Cremonesi - Recommender Systems
  13. 13. 13 Memory vs. model based Memory Model based based User-based X Item-based X Dimensional- X reduction Content-based X Paolo Cremonesi - Recommender Systems
  14. 14. 14 Collaborative Filtering User-based similar users rate an item 4 5 similarly ? 3 Item-based similar items are rated by a user similarly 2 2 Neighborhood NB: similarity means correlation Paolo Cremonesi - Recommender Systems
  15. 15. 15 Collaborative filtering: User Rating Matrix User Item Paolo Cremonesi - Recommender Systems
  16. 16. 16 User rating matrix URM I1 I2 I3 I4 I1 I2 I3 I4 U1 3 4 0 1 U1 0 1 0 1 U2 2 2 1 0 U2 0 0 1 1 U3 2 0 0 4 U3 1 1 0 0 U4 1 5 0 1 U4 1 1 0 1 U5 3 0 1 0 U5 0 1 1 1 Explicit URM Implicit URM Paolo Cremonesi - Recommender Systems
  17. 17. 17 Dimensional-reduction collaborative model • items and users can be described by a number (K) of unknown features • auf : describes if feature f is important for user u • bif : describes if feature f is present in item i • rui : rating assigned by (or estimated) user u to item i k rui = f =1 auf · bif Paolo Cremonesi - Recommender Systems
  18. 18. 18 Singular Value Decomposition mxn R A = U S VTT V mxn mxk kxk kxn Sk VkT Rk A = Uk Paolo Cremonesi - Recommender Systems
  19. 19. 19 Singular Value Decomposition T R=U·S·V T V·V =I T U ·U=I Paolo Cremonesi - Recommender Systems
  20. 20. 20 Singular Value Decomposition T R k = U k · Sk · Vk T Vk · Vk =I T Uk · Uk = I Rk : best rank-k approximation of R according to the Frobenious norm not according least square error!! Paolo Cremonesi - Recommender Systems
  21. 21. 21 Folding-in • New rows/columns of A are projected (folded-in) in the existing latent space without computing a new SVD • e.g., a new user u u’ = u Vk Sk-1 Sk Vk Ak Uk u u’ Paolo Cremonesi - Recommender Systems
  22. 22. 22 Collaborative Filtering: pro & cons • Pro: There is no need for content • Cons: Cold Start: we needs to have enough users in the system to find a match. Sparsity: when the user/ratings matrix is sparse it is hard to find a neighbourhood. First Rater: cannot recommend an item that has not been previously rated anyone else Popularity Bias: cannot recommend items to someone with unique tastes. Tends to recommend popular items (dataset coverage) Paolo Cremonesi - Recommender Systems
  23. 23. 23 Content-based Filtering Term 3 ...mettendo a punto una scoperta che potrebbe portare al primo uso terapeutico della controversa procedura. Se gli studi animali si riveleranno promettenti, i ricercatori potrebbero cominciare a mettere alla prova le nuove cellule su occhi umani da qui a due anni... 2 Term 1 m Ter • Similar items contain the same terms • The more a term occurs in an item, the more representative it is • The more a term occurs in the collection, the less representative it is (i.e. it is less important in order to distinguish a specific item) Paolo Cremonesi - Recommender Systems
  24. 24. 24 Content-based filtering: Item-Content Matrix Word Item Paolo Cremonesi - Recommender Systems
  25. 25. 25 Content-based Filtering: techniques Term 3 User-item similarity Term 1 2 m Ter Paolo Cremonesi - Recommender Systems
  26. 26. 26 Content-based Filtering: pro & cons • Pro: No need for data on other users No cold-start or sparsity problems, neither first-rater • Able to recommend to users with unique tastes • Able to recommend new and unpopular items Can provide explanations about recommended items Well-known technology • Cons: Requires a structured content Lower accuracy Users tastes must be represented as a function of the content Unable to exploit quality judgments of other users Paolo Cremonesi - Recommender Systems
  27. 27. 27 Content-based Filtering: Latent Semantic Analysis svd Sk Vk T A Ak = Uk mxn -Terms in rows Vk * sqrt (Sk) -Items in columns Uk * sqrt (Sk) pseudo terms pseudo items cosine Ak Paolo Cremonesi - Recommender Systems
  28. 28. 28 Recommender architecture Resources management Features Features Items Storage extraction representation Filter Compute user- Items Items item correlation retrieval recommendation Users management Infer and learn Interests/tastes Users profile representation feedback Explicit vs implicit ratings Paolo Cremonesi - Recommender Systems
  29. 29. 29 Datasets Real datasets composed by movies and user fruitions, plus some extra information • User-item rating matrix 23942 users 564 movies 56686 ratings • Movie Meta-data (textual information) Title Genre Director Cast Duration … Paolo Cremonesi - Recommender Systems
  30. 30. 30 • Implicit vs Explicit • Come determinare il rating implicito VOD TV (EPG) Paolo Cremonesi - Recommender Systems
  31. 31. 31 Some problems with IPTV recommender • Cold start • Multi-language content (e.g., Switzerland) • New user problem (user-based algorithms) • New item problem (all collaborative algorithms) • Semantic problem (e.g., house and home) Paolo Cremonesi - Recommender Systems
  32. 32. 32 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
  33. 33. 33 Problem • Many works do not describe clearly the methods used for performance evaluation and model comparison • Different dataset partition methodology and evaluation metrics lead to divergent results • The Netflix prize has improperly focused the research attention on Hold-out RMSE Paolo Cremonesi - Recommender Systems
  34. 34. 34 Objective • Design a new methodology to compare different algorithms according to 34 how often the user watches the TV (length of user profile) if the user prefers “blockbuster” movies (user preference versus popular or unpopular movies and programs) • Design a multi-model system Paolo Cremonesi - Recommender Systems
  35. 35. 35 Metrics • Error metrics • Accuracy metrics Mean Square Error Recall (MSE) Precision Root Mean Square Error Fallout (RMSE) F-measure Mean Absolute Error (MAE) ☺ Both implicit and explicit datasets Only for explicit datasets Top-N recommender systems Paolo Cremonesi - Recommender Systems
  36. 36. 36 Accuracy metrics Paolo Cremonesi - Recommender Systems
  37. 37. 37 Accuracy metrics Paolo Cremonesi - Recommender Systems
  38. 38. 38 Accuracy metrics Paolo Cremonesi - Recommender Systems
  39. 39. 39 Netflix dataset: test user profile Paolo Cremonesi - Recommender Systems
  40. 40. 40 Netflix dataset: Global effects algorithm RMSE: 0.95 Recall: 1% F-measure: 0.01 Paolo Cremonesi - Recommender Systems
  41. 41. 41 Netflix dataset: Adjusted cosine algorithm RMSE: 1.6 Recall: 8% F-measure: 0.16 Paolo Cremonesi - Recommender Systems
  42. 42. 42 Netflix dataset: SVD algorithm RMSE: 2.7 Recall: 17% F-measure: 0.28 Paolo Cremonesi - Recommender Systems
  43. 43. 43 Quality evaluation • Focus on future performance on new data • Proper partitioning of original data set into: 43 training set test set • Test set must be different and independent from training set • Active user: should be left out of the model Paolo Cremonesi - Recommender Systems
  44. 44. 44 Hold-out Paolo Cremonesi - Recommender Systems
  45. 45. 45 Leave-one-out Paolo Cremonesi - Recommender Systems
  46. 46. 46 K-fold Paolo Cremonesi - Recommender Systems
  47. 47. 47 Agenda • IPTV architecture • Recommender algorithms • Evaluation of different algorithms • Multi-model systems Paolo Cremonesi - Recommender Systems
  48. 48. 48 Recommender system architecture Inputs Real time calls Business Rules Web Items’ Content Services STB client (ICM) Batch Real-time STB … Processing Recommendation server STB client Users’ Ratings (URM) Model Repository Paolo Cremonesi - Recommender Systems
  49. 49. Proposed approach • Batch system Statistical analysis of the dataset Definition of a number of models Accuracy evaluation for different user profiles • Run-time system User profile analysis Selection of best candidate model Recommendation Paolo Cremonesi - Recommender Systems
  50. 50. Multi-model recommender engine Paolo Cremonesi - Recommender Systems
  51. 51. 51 Dataset statistical analysis (example) Paolo Cremonesi - Recommender Systems
  52. 52. 52 Dataset statistical analysis (example) 1 Percentage of rated items in the top-rated NM ML 0.75 NF 0.5 0.25 0 0 1 2 3 4 10 10 10 10 10 Position of the items in the top-rated Paolo Cremonesi - Recommender Systems
  53. 53. 53 Dataset statistical analysis (example) User groups Item popularity 20 or more Popular 10...19 2...9 Non-Popular Paolo Cremonesi - Recommender Systems
  54. 54. 54 Popular vs. unpopular: SVD algorithm - NF 0.35 all 0.3 popular unpopular 0.25 0.2 Recall 0.15 0.1 0.05 0 0 200 400 600 800 1000 Latent size Paolo Cremonesi - Recommender Systems
  55. 55. 55 Popular vs. unpopular: SVD algorithm - NM all popular 0.25 unpopular 0.2 Recall 0.15 0.1 0.05 5 15 50 100 200 300 Latent size Paolo Cremonesi - Recommender Systems
  56. 56. 56 User profile length – NM recall Group SVD Cos -like NBN_S NBN_I NBN_U 2 -9 11,21% 19,35% 19,65% 17,23% 21,65% All 10 -19 11,23% 13,11% 12,09% 13,62% 12,60% 20 -inf 9,91% 8,01% 6,45% 6,65% 6,52% Group SVD Cos -like NBN_S NBN_I NBN_U 2 -9 22,21% 31,21% 31,54% 26,17% 33,93% Popular 10 -19 24,12% 27,12% 24,61% 27,36% 25,59% 20 -inf 25,72% 22,71% 20,71% 21,14% 20,93% Group SVD Cos -like NBN_S NBN_I NBN_U Unpopular 2 -9 9,92% 0,81% 0,13% 2,64% 1,48% 10 -19 10,01% 1,23% 0,19% 0,56% 0,25% 20 -inf 10,14% 0,70% 0,01% 0,10% 0,01% Best average algorithm (item-based) 15,94% Multi-model (overall) 20,92% Paolo Cremonesi - Recommender Systems

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