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RecSys: Recommender Systems
            Tran The Truyen
            http://truyen.vietlabs.com
The world is an over-crowded place
They all want to get our attention
We are overloaded
• Thousands of news articles
  and blog posts each day
• Millions of movies, books
  and music tracks online
• In Hanoi, > 50 TV channels,
  thousands of programs
  each day
• In New York, several
  thousands of ad messages
  sent to us per day
But we really need and
consume only a few of them!
Sometimes, all we need is this
Or, just this


                 !
               RB
             TU
           IS
         D
    ’T
   N
  O
D
Help me!
Can Google help?
• Yes, but only when we really know what
 we are looking for
• What if I just want some interesting music
 tracks?
  – Btw, what does it mean by “interesting”?
Can Facebook help?
• Yes, I tend to find my friends’ stuffs
  interesting
• What if I had only few friends, and what
  they like do not always attract me?
Can experts help?
• Yes, but it won’t scale well
  – Everyone receives exactly the same advice!

• It is what they like, not me!
  – Like movies, what get expert approval does
    not guarantee attention of the mass
OK, here is the idea called RecSys:
                                I like these bits
• To recommend to us
  something we may like
  – It may not be popular
  – The world is long-tailed
• How?
  – Based on our history of
    using services
  – Based on other people
    like us
  – Ever heard of “collective
    intelligence”?
Hang on, what is long-tailed?
• Popularised by Chris Anderson, Wired 2004

                          The short-tailed distribution


                           The bell-shaped distribution




                                 The long-tailed distribution
Ever heard of
• GroupLens?
• Amazon recommendation?
• Netflix Cinematch?
• Google News personalization?
• Netflix Prize $1mil challenge?
• Strands?
• TiVo?
• Findory?
Recommender Systems
Want some evidences?
             (Celma & Lamere, ISMIR 2007)


• Netflix:
  – 2/3 rented movies are from recommendation

• Google News
  – 38% more click-through are due to
    recommendation

• Amazon
  – 35% sales are from recommendation
What can be recommended?
• Advertising messages • Tags
• Investment choices   • News articles
• Restaurants          • Online mates (Dating services)
• Cafes                • Future friends (Social network sites)
• Music tracks         • Courses in e-learning
• Movies               • Drug components
• TV programs          • Research papers
• Books                • Citations
• Cloths               • Code modules
• Supermarket goods    • Programmers
But, what do recommender
         systems do, exactly?
1. Predict how much you may like a certain
   product/service
2. Compose a list of N best items for you
3. Compose a list of N best users for a certain
   product/service
4. Explain to you why these items are recommended to
   you
5. Adjust the prediction and recommendation based on
   your feedback and other people
Graph representation

      Titanic               Taken         Panda




                                    ?
Me              My friend           You           Another guy
We must also take a good care of

• Data normalisation
• Removal or reduction of noise
• Protection of users’ privacy
• Attack: someone just doesn’t like your
 system
Task 1: Preference prediction
• Collaborative filtering
  – User-based method
  – Item-based method
  – Matrix Factorization
• Content-based filtering
• Hybrid:
  – Linear/sequential/switching combination
  – Semi-Restricted Boltzmann Machines
Collaborative filtering (1)
• User-based method (1994,
  GroupLens)
  – Many people liked “Kungfu
    Panda”                                                     item
                                                123    4   5678
  – Can you tell how much I like it?
                                              1545         3       4
  – The idea is to pick about 20-50
                                              2  35          4    5
    people who share similar
                                              3  4     5   4
    taste with me, then how much I
                                              45       5     35
                                                   4
    like depend on how much
                                              54           33      4
    THEY liked.
                                              652          35
  – In short: you may like it
                                              7    1   4   2
                                       user
    because your “friends” liked it
                                              8        5       43
Collaborative filtering (2)
• Item-based method (2001,
  deployed at Amazon)
   – I have watched so many good &
     bad movies
   – Would you recommend me
     watching “Taken”?                                                         item
   – The idea is to pick from my                       1   2   3   4   5   67         8
     previous list 20-50 movies that
                                                   1       4           3           4
                                                       5       5
     share similar audience with
     “Taken”, then how much I will like            2       3   5           4       5
     depend on how much I liked those              3       4       5   4
     early movies
                                                   4               5       35
                                                       5       4
   – In short: I tend to watch this movie
     because I have watched those                  5   4               3   3       4
     movies … or                                   6   5   2           3   5
   – People who have watched those
                                                   7           1   4   2
                                            user

     movies also liked this movie
     (Amazon style)                                8               5           4   3
Collaborative filtering (3)
~ [0.1 0.3 0.2 0.9 0.5 0.4 0.7 0.3 0.8 1.5]
• Matrix Factorization (2006, Netflix
  challgence)
  – You many have watched thousands of movies
  – But perhaps I can tell these movies belong to
    10 groups, like Action, Sci-Fi, Animation,
    etc,…
  – So 10 numbers are enough to describe your
    taste
  – Likewise, “Titanic” has been watched by
    millions people, but perhaps …10 numbers
    are enough to describe its features
  – Magic: these hidden aspects can be
    discovered automatically by Matrix
    Factorization!
Problems with collaborative filtering
• Scale
   – Netflix (2007): 5M users, 50K movies, 1.4B ratings

• Sparse data
   – I have rated only one book at Amazon!

• Cold-Start
   – New users and items do not have history

• Popularity bias
   – Everyone reads “Harry Potter”

• Hacking
   – Someone reads “Harry Potter” reads “Karma Sutra”
Content-based method
• Web page: words, hyperlinks, images, tags, comments,
  titles, URL, topic
• Music: genre, rhythm, melody, harmony, lyrics, meta data,
  artists, bands, press releases, expert reviews, loudness,
  energy, time, spectrum, duration, frequency, pitch, key,
  mode, mood, style, tempo
• User: age, sex, job, location, time, income, education,
  language, family status, hobbies, general interests, Web
  usage, computer usage, fan club membership, opinion,
  comments, tags, mobile usage
• Context: time, location, mobility, activity, socializing,
  emotion
Content-based method (2)
• Can we acquire those content pieces
  automatically?
  – Fairly easy for text
  – Difficult for music and video, except for digital signals.
    E.g. music genre classification 60-80% accuracy
  – A lot of noise, e.g. misplaced tags
  – Attacks
• What can we do with these?
  – Compute similarity between items or users
  – Query items that are similar to a given item
  – Match item’s content and user’s profile
Content-based method (3)
• Measuring similarity
  – Cosine, TF-IDF as in standard Information
    Retrieval
  – KL-divergence for probability-oriented guys
  – Euclidean, dimensionality reduction if you
    want
  – Anything you can imagine of!
Hybrid: Semi-Restricted Boltzmann
       Machines (2009, IMPCA)
                                         User A             User B   User C

• A probabilistic combination of
   –   Item-based method
   –   User-based method
   –   Matrix Factorization
   –   (May be) content-based method

• It looks like a Neural Network
                                              11
                                              00              111
                                                              000
   – But it does not really so ☺              11
                                              00              111
                                                              000
                                              11
                                              00              111
                                                              000
                                              11
                                              00              111
                                                              000
                                                   Item X

• It really is a type of Markov
  random fields, which is, again, a
  type of Graphical Models
   – Self-advertising: I work on these
     stuffs for living!
But, what do recommender
         systems do, exactly?
1. Predict how much you may like a certain
   product/service
2. Compose a list of N best items for you
3. Compose a list of N best users for a certain
   product/service
4. Explain to you why these items are recommended to
   you
5. Adjust the prediction and recommendation based on
   your feedback and other people
Task 2,3: Top-N recommendation

• Top-N item list:
   – Find similar users, collect what they like
   – Filter out those the user has rated
   – Rank the remaining items by considering
      •   The number of times each item is liked by those users
      •   The popularity of the item
      •   The associated ratings
      •   The similarity between each item in the list and what the user
          has rated

• Switching the role of item to user, we may have
  top-N user list
But, what do recommender
         systems do, exactly?
1. Predict how much you may like a certain
   product/service
2. Compose a list of N best items for you
3. Compose a list of N best users for a certain
   product/service
4. Explain to you why these items are recommended to
   you
5. Adjust the prediction and recommendation based on
   your feedback and other people
Task 4: Explanation
• This is a current hit …
• More on this artist …
• Try something from similar artists …
• Someone similar to you also like this …
• As you listened to that, you may want this …
• These two go together …
• This is most popular in your group …
• This is highly rated …
• Try something new …
Task 4: Explanation (2)
• Examples from Strands.com
  –   Welcom back (recently viewed)
  –   For you today
  –   New for you
  –   Hot / Most popular of this type
  –   Other people also do this …
  –   Similar or related products
  –   Complementary accessories
  –   This goes with this …
  –   Gift idea
  –   Shopping assisant
But, what do recommender
         systems do, exactly?
1. Predict how much you may like a certain
   product/service
2. Compose a list of N best items for you
3. Compose a list of N best users for a certain
   product/service
4. Explain to you why these items are recommended to
   you
5. Adjust the prediction and recommendation based on
   your feedback and other people
Task 5: Online updating
• New items and users come each hour or minute
• The two worlds:
  – Most songs and books are still interesting for a long
    time (the tail is really long)
  – Most news articles are read on the day and forgotten
    next day
     • But tracking back is useful to follow an event or scandal

• Online updating large-scale neighbour-based
  systems is NOT easy at all
Evaluation
• How do we know the recommendation is
  good?
  – How good is good?
  – Measures should be automated
• Practice: training/testing split (e.g. 80/20)
• Popular criteria
  – Prediction error: ZOE, MAE, RMSE
  – Hit recall/precision/F-measure, rank utility,
    ROC curve,
Evaluation (2)
• Yet little on
  – Relevance
  – Usefulness
  – % Increase in purchase
  – % Reduction in cost
  – Novelty/surprise/long-tails
  – Diversity
  – Coverage
  – Explainability
A question: Can we
make use of these
information sources?
• Blogs
• Social Media
• Online comments
• Online stores
• Review sites
• Locations
• Mobility
A case-study: Strands
• Services for any online-retailers
   – Retailers send product, purchase information into
     Strands server (one retailer per account) through
     APIs
   – Strands returns recommendation for each visitor
• The same logic for social media servers
• moneyStrands for personal financial
  management (e.g. investment recommendation)
• MyStrands for music personalization
Want more practical hints?
• New books:
  – Toby Segaran, Programming Collective
    Intelligence, O'Reilly, 2007
  – Satnam Alag, Collective Intelligence in
    Action, Manning Publications, 2009
• Check out for real deployment:
  – TechCrunch
  – ReadWriteWeb
Want more state-of-the-arts?
• Research in Recommender Systems is becoming a
  mainstream, evidenced from the recent conference
  ACM RecSys.
• Other places:
  –   ICWSM: Weblog and Social Media
  –   WebKDD: Web Knowledge Discovery and Data Mining
  –   WWW: The original WWW conference
  –   SIGIR: Information Retrieval
  –   ACM KDD: Knowledge Discovery and Data Mining
  –   ICML: Machine Learning
Questions left to you
• Will you trust such Recommender
 Systems?
• Will you implement and deploy it here?
• Will you do research?
  – PhD scholarships available (as of 19/4/09)
  – See http://truyen.vietlabs.com/scholarship.html
  – Warning: you are going to waste 3-5 years of your
    youth life!

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Recommender Systems

  • 1. RecSys: Recommender Systems Tran The Truyen http://truyen.vietlabs.com
  • 2. The world is an over-crowded place
  • 3. They all want to get our attention
  • 4. We are overloaded • Thousands of news articles and blog posts each day • Millions of movies, books and music tracks online • In Hanoi, > 50 TV channels, thousands of programs each day • In New York, several thousands of ad messages sent to us per day
  • 5. But we really need and consume only a few of them!
  • 6. Sometimes, all we need is this
  • 7. Or, just this ! RB TU IS D ’T N O D
  • 9. Can Google help? • Yes, but only when we really know what we are looking for • What if I just want some interesting music tracks? – Btw, what does it mean by “interesting”?
  • 10. Can Facebook help? • Yes, I tend to find my friends’ stuffs interesting • What if I had only few friends, and what they like do not always attract me?
  • 11. Can experts help? • Yes, but it won’t scale well – Everyone receives exactly the same advice! • It is what they like, not me! – Like movies, what get expert approval does not guarantee attention of the mass
  • 12. OK, here is the idea called RecSys: I like these bits • To recommend to us something we may like – It may not be popular – The world is long-tailed • How? – Based on our history of using services – Based on other people like us – Ever heard of “collective intelligence”?
  • 13. Hang on, what is long-tailed? • Popularised by Chris Anderson, Wired 2004 The short-tailed distribution The bell-shaped distribution The long-tailed distribution
  • 14. Ever heard of • GroupLens? • Amazon recommendation? • Netflix Cinematch? • Google News personalization? • Netflix Prize $1mil challenge? • Strands? • TiVo? • Findory?
  • 16. Want some evidences? (Celma & Lamere, ISMIR 2007) • Netflix: – 2/3 rented movies are from recommendation • Google News – 38% more click-through are due to recommendation • Amazon – 35% sales are from recommendation
  • 17. What can be recommended? • Advertising messages • Tags • Investment choices • News articles • Restaurants • Online mates (Dating services) • Cafes • Future friends (Social network sites) • Music tracks • Courses in e-learning • Movies • Drug components • TV programs • Research papers • Books • Citations • Cloths • Code modules • Supermarket goods • Programmers
  • 18. But, what do recommender systems do, exactly? 1. Predict how much you may like a certain product/service 2. Compose a list of N best items for you 3. Compose a list of N best users for a certain product/service 4. Explain to you why these items are recommended to you 5. Adjust the prediction and recommendation based on your feedback and other people
  • 19. Graph representation Titanic Taken Panda ? Me My friend You Another guy
  • 20. We must also take a good care of • Data normalisation • Removal or reduction of noise • Protection of users’ privacy • Attack: someone just doesn’t like your system
  • 21. Task 1: Preference prediction • Collaborative filtering – User-based method – Item-based method – Matrix Factorization • Content-based filtering • Hybrid: – Linear/sequential/switching combination – Semi-Restricted Boltzmann Machines
  • 22. Collaborative filtering (1) • User-based method (1994, GroupLens) – Many people liked “Kungfu Panda” item 123 4 5678 – Can you tell how much I like it? 1545 3 4 – The idea is to pick about 20-50 2 35 4 5 people who share similar 3 4 5 4 taste with me, then how much I 45 5 35 4 like depend on how much 54 33 4 THEY liked. 652 35 – In short: you may like it 7 1 4 2 user because your “friends” liked it 8 5 43
  • 23. Collaborative filtering (2) • Item-based method (2001, deployed at Amazon) – I have watched so many good & bad movies – Would you recommend me watching “Taken”? item – The idea is to pick from my 1 2 3 4 5 67 8 previous list 20-50 movies that 1 4 3 4 5 5 share similar audience with “Taken”, then how much I will like 2 3 5 4 5 depend on how much I liked those 3 4 5 4 early movies 4 5 35 5 4 – In short: I tend to watch this movie because I have watched those 5 4 3 3 4 movies … or 6 5 2 3 5 – People who have watched those 7 1 4 2 user movies also liked this movie (Amazon style) 8 5 4 3
  • 24. Collaborative filtering (3) ~ [0.1 0.3 0.2 0.9 0.5 0.4 0.7 0.3 0.8 1.5] • Matrix Factorization (2006, Netflix challgence) – You many have watched thousands of movies – But perhaps I can tell these movies belong to 10 groups, like Action, Sci-Fi, Animation, etc,… – So 10 numbers are enough to describe your taste – Likewise, “Titanic” has been watched by millions people, but perhaps …10 numbers are enough to describe its features – Magic: these hidden aspects can be discovered automatically by Matrix Factorization!
  • 25. Problems with collaborative filtering • Scale – Netflix (2007): 5M users, 50K movies, 1.4B ratings • Sparse data – I have rated only one book at Amazon! • Cold-Start – New users and items do not have history • Popularity bias – Everyone reads “Harry Potter” • Hacking – Someone reads “Harry Potter” reads “Karma Sutra”
  • 26. Content-based method • Web page: words, hyperlinks, images, tags, comments, titles, URL, topic • Music: genre, rhythm, melody, harmony, lyrics, meta data, artists, bands, press releases, expert reviews, loudness, energy, time, spectrum, duration, frequency, pitch, key, mode, mood, style, tempo • User: age, sex, job, location, time, income, education, language, family status, hobbies, general interests, Web usage, computer usage, fan club membership, opinion, comments, tags, mobile usage • Context: time, location, mobility, activity, socializing, emotion
  • 27. Content-based method (2) • Can we acquire those content pieces automatically? – Fairly easy for text – Difficult for music and video, except for digital signals. E.g. music genre classification 60-80% accuracy – A lot of noise, e.g. misplaced tags – Attacks • What can we do with these? – Compute similarity between items or users – Query items that are similar to a given item – Match item’s content and user’s profile
  • 28. Content-based method (3) • Measuring similarity – Cosine, TF-IDF as in standard Information Retrieval – KL-divergence for probability-oriented guys – Euclidean, dimensionality reduction if you want – Anything you can imagine of!
  • 29. Hybrid: Semi-Restricted Boltzmann Machines (2009, IMPCA) User A User B User C • A probabilistic combination of – Item-based method – User-based method – Matrix Factorization – (May be) content-based method • It looks like a Neural Network 11 00 111 000 – But it does not really so ☺ 11 00 111 000 11 00 111 000 11 00 111 000 Item X • It really is a type of Markov random fields, which is, again, a type of Graphical Models – Self-advertising: I work on these stuffs for living!
  • 30. But, what do recommender systems do, exactly? 1. Predict how much you may like a certain product/service 2. Compose a list of N best items for you 3. Compose a list of N best users for a certain product/service 4. Explain to you why these items are recommended to you 5. Adjust the prediction and recommendation based on your feedback and other people
  • 31. Task 2,3: Top-N recommendation • Top-N item list: – Find similar users, collect what they like – Filter out those the user has rated – Rank the remaining items by considering • The number of times each item is liked by those users • The popularity of the item • The associated ratings • The similarity between each item in the list and what the user has rated • Switching the role of item to user, we may have top-N user list
  • 32. But, what do recommender systems do, exactly? 1. Predict how much you may like a certain product/service 2. Compose a list of N best items for you 3. Compose a list of N best users for a certain product/service 4. Explain to you why these items are recommended to you 5. Adjust the prediction and recommendation based on your feedback and other people
  • 33. Task 4: Explanation • This is a current hit … • More on this artist … • Try something from similar artists … • Someone similar to you also like this … • As you listened to that, you may want this … • These two go together … • This is most popular in your group … • This is highly rated … • Try something new …
  • 34. Task 4: Explanation (2) • Examples from Strands.com – Welcom back (recently viewed) – For you today – New for you – Hot / Most popular of this type – Other people also do this … – Similar or related products – Complementary accessories – This goes with this … – Gift idea – Shopping assisant
  • 35. But, what do recommender systems do, exactly? 1. Predict how much you may like a certain product/service 2. Compose a list of N best items for you 3. Compose a list of N best users for a certain product/service 4. Explain to you why these items are recommended to you 5. Adjust the prediction and recommendation based on your feedback and other people
  • 36. Task 5: Online updating • New items and users come each hour or minute • The two worlds: – Most songs and books are still interesting for a long time (the tail is really long) – Most news articles are read on the day and forgotten next day • But tracking back is useful to follow an event or scandal • Online updating large-scale neighbour-based systems is NOT easy at all
  • 37. Evaluation • How do we know the recommendation is good? – How good is good? – Measures should be automated • Practice: training/testing split (e.g. 80/20) • Popular criteria – Prediction error: ZOE, MAE, RMSE – Hit recall/precision/F-measure, rank utility, ROC curve,
  • 38. Evaluation (2) • Yet little on – Relevance – Usefulness – % Increase in purchase – % Reduction in cost – Novelty/surprise/long-tails – Diversity – Coverage – Explainability
  • 39. A question: Can we make use of these information sources? • Blogs • Social Media • Online comments • Online stores • Review sites • Locations • Mobility
  • 40. A case-study: Strands • Services for any online-retailers – Retailers send product, purchase information into Strands server (one retailer per account) through APIs – Strands returns recommendation for each visitor • The same logic for social media servers • moneyStrands for personal financial management (e.g. investment recommendation) • MyStrands for music personalization
  • 41. Want more practical hints? • New books: – Toby Segaran, Programming Collective Intelligence, O'Reilly, 2007 – Satnam Alag, Collective Intelligence in Action, Manning Publications, 2009 • Check out for real deployment: – TechCrunch – ReadWriteWeb
  • 42. Want more state-of-the-arts? • Research in Recommender Systems is becoming a mainstream, evidenced from the recent conference ACM RecSys. • Other places: – ICWSM: Weblog and Social Media – WebKDD: Web Knowledge Discovery and Data Mining – WWW: The original WWW conference – SIGIR: Information Retrieval – ACM KDD: Knowledge Discovery and Data Mining – ICML: Machine Learning
  • 43. Questions left to you • Will you trust such Recommender Systems? • Will you implement and deploy it here? • Will you do research? – PhD scholarships available (as of 19/4/09) – See http://truyen.vietlabs.com/scholarship.html – Warning: you are going to waste 3-5 years of your youth life!