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Overview of recommender system

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Overview of recommender system

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Overview of recommender system

  1. 1. Overview of Recommender System STANLEY WANG SOLUTION ARCHITECT, TECH LEAD @SWANG68 http://www.linkedin.com/in/stanley-wang-a2b143b
  2. 2. Recommender System
  3. 3. What is Recommender System?
  4. 4. Feedback to Recommender System
  5. 5. Which Areas can Recommender Benefit?
  6. 6. Typical Architecture of Recommender System
  7. 7. Recommender System Types • Collaborative Filtering System – aggregation of consumers’ preferences and recommendations to other users based on similarity in behavioral patterns; • Content-based System – supervised machine learning used to induce a classifier to discriminate between interesting and uninteresting items for the user; • Knowledge-based System – knowledge about users and products used to reason what meets the user’s requirements, using discrimination tree, decision support tools, case-based reasoning ;
  8. 8. Paradigms of Recommender: Collaborative Filtering Collaborative: "Tell me what's popular among my peers"
  9. 9. Paradigms of Recommender : Content Based Content-based: "Show me more of the same what I've liked"
  10. 10. Paradigms of Recommender : Knowledge Based Knowledge-based: "Tell me what fits based on my needs"
  11. 11. Paradigms of Recommender : Hybrid Hybrid: combinations of various inputs and/or composition of different mechanism
  12. 12. Technology Evolution of Recommender
  13. 13. abcd People who liked this also liked ….. How Collaborative Filtering works? 13 Item to Item User to User abcd User-to-User  Recommendations are made by finding users with similar tastes. Jane and Tim both liked Item 2 and disliked Item 3; it seems they might have similar taste, which suggests that in general Jane agrees with Tim. This makes Item 1 a good recommendation for Tim. This approach does not scale well for millions of users. Item-to-Item  Recommendations are made by finding items that have similar appeal to many users. Tom and Sandra are two users who liked both Item 1 and Item 4. That suggests that, in general, people who liked Item 4 will also like item 1, so Item 1 will be recommended to Tim. This approach is scalable to millions of users and millions of items.
  14. 14. Collaborative Filtering • The most prominent approach to generate recommendations o used by large, commercial e-commerce sites o well-understood, various algorithms and variations exist o applicable in many domains (book, movies, songs, ..) • Approach o use the "wisdom of the crowd" to recommend items • Basic assumption and idea o Users give ratings to catalog items (implicitly or explicitly) o Customers who had similar tastes in the past, will have similar tastes in the future
  15. 15. Collaborative Filtering Toolkit • Implemented Big Graph ML Algorithms, including: o Alternative Least Squares (ALS) o Sparse-ALS o SVD++ o LibFM (factorization machines) o GenSGD o Item-similarity based methods
  16. 16. User-based Nearest-Neighbor CF • The basic technique: o Given an "active user" (Alice) and an item I not yet seen by Alice o The goal is to estimate Alice's rating for this item, e.g., by • find a set of users (peers) who liked the same items as Alice in the past and who have rated item I • use, e.g. the average of their ratings to predict, if Alice will like item I • do this for all items Alice has not seen and recommend the best-rated
  17. 17. User-based Nearest-Neighbor CF • Some first questions o How do we measure similarity? o How many neighbors should we consider? o How do we generate a prediction from the neighbors' ratings? Item1 Item2 Item3 Item4 Item5 Alice 5 3 4 4 ? User1 3 1 2 3 3 User2 4 3 4 3 5 User3 3 3 1 5 4 User4 1 5 5 2 1
  18. 18. Commonly Used Similarity Measure
  19. 19. KNN Nearest Neighbour Methods • unseen item needed to be classified • positive rated items • negative rated items • k = 3: negative • k = 5: positive A user-based kNN collaborative filtering method consists of two primary phases: • the neighborhood formation phase • the recommendation phase
  20. 20. Measuring user similarity • A popular similarity measure in user-based CF: Pearson correlation a, b : users ra,p : rating of user a for item p P : set of items, rated both by a and b Possible similarity values between -1 and 1; = user's average ratings Item1 Item2 Item3 Item4 Item5 Alice 5 3 4 4 ? User1 3 1 2 3 3 User2 4 3 4 3 5 User3 3 3 1 5 4 User4 1 5 5 2 1 sim = 0,85 sim = 0,70 sim = -0,79 𝒓 𝒂, 𝒓 𝒃
  21. 21. Making predictions • A common prediction function: • Calculate, whether the neighbors' ratings for the unseen item i are higher or lower than their average • Combine the rating differences – use the similarity as a weight • Add/subtract the neighbors' bias from the active user's average and use this as a prediction
  22. 22. Item-based Collaborative Filtering • Basic idea: o Use the similarity between items (and not users) to make predictions • Example: o Look for items that are similar to Item5 o Take Alice's ratings for these items to predict the rating for Item5 Item1 Item2 Item3 Item4 Item5 Alice 5 3 4 4 ? User1 3 1 2 3 3 User2 4 3 4 3 5 User3 3 3 1 5 4 User4 1 5 5 2 1
  23. 23. Pre-processing for Item-based CF • Item-based filtering does not solve the scalability problem itself • Pre-processing approach by Amazon.com in 2003 o Calculate all pair-wise item similarities in advance o The neighborhood to be used at run-time is typically rather small, because only items are taken into account which the user has rated o Item similarities are supposed to be more stable than user similarities • Memory requirements o Up to N2 pair-wise similarities to be memorized (N = number of items) in theory o In practice, this is significantly lower (items with no co-ratings) o Further reductions possible • Minimum threshold for co-ratings (items, which are rated at least by n users) • Limit the size of the neighborhood (might affect recommendation accuracy)
  24. 24. Similarity Measure for Item based CF • Produces better results in item-to-item filtering o for some datasets, no consistent picture in literature • Ratings are seen as vector in n-dimensional space • Similarity is calculated based on the angle between the vectors • Adjusted cosine similarity o take average user ratings into account, transform the original ratings o U: set of users who have rated both items a and b
  25. 25. Recommendation for Item-based CF • After computing the similarity between items we select a set of k most similar items to the target item and generate a predicted value of user u’s rating where J is the set of k similar items       Jj Jj j jisim jisimr ip ),( ),( )( ,u u,
  26. 26. What is Latent Factor Model? Latent variables are introduced to account for the underlying reasons of a user’s choice. When the connections between the latent variables and observed variables (user, product, rating, etc.) are estimated during the training recommendations can be made to users by computing their possible interactions with each product through the latent variables;
  27. 27. Matrix Factorization Approach
  28. 28. How does LSM Work?
  29. 29. Latent Factor Model Algorithm
  30. 30. LSM Algorithm : Alternating Least Square
  31. 31. LSM Algorithm : Alternating Least Square
  32. 32. LSM Algorithm : Stochastic Gradient Descent
  33. 33. Context-Based Recommender Systems Overview  The recommender system uses additional data about the context of an item consumption;  For example, in the case of a restaurant the time or the location may be used to improve the recommendation compared to what could be performed without this additional source of information;  A restaurant recommendation for a Saturday evening when you go with your spouse should be different than a restaurant recommendation on a workday afternoon when you go with co- workers; abcd Overview
  34. 34. Context-Based Recommender Systems  Recommend a vacation  Winter vs. summer  Recommend a purchase (e-retailer)  Gift vs. for yourself  Recommend a movie  To a student who wants to watch it on Saturday night with his girlfriend in a movie theater. Motivating Examples 35
  35. 35.  Recommend music  The music that we like to hear is greatly affected by a context, such that can be thought of a mixture of our feelings (mood) and the situation or location (the theme) we associate it with.  Listen to Bruce Springteen "Born in USA" while driving along the 101.  Listening to Mozart's Magic Flute while walking in Salzburg. Motivating Examples Context-Based Recommender Systems 36
  36. 36.  What is the user when asking for a recommendation?  Where (and when) the user is ?  What does the user (e.g., improve his knowledge or really buy a product)?  Is the user or with other ?  Are there products to choose or only ? Plain recommendation technologies forget to take into account the user context. Context-Based Recommender Systems What simple recommendation techniques ignore? 37
  37. 37.  Obtain sufficient and reliable data describing the user context  Selecting the right information, i.e., relevant in a particular personalization task  Understand the impact of contextual dimensions on the personalization process  Computational model the contextual dimension in a more classical recommendation technology  For instance: how to extend Collaborative Filtering to include contextual dimensions? abcd Major obstacle for contextual computing Context-Based Recommender Systems 38
  38. 38.  Each item in the data base is a candidate for splitting  Context defines all possible splits of an item ratings vector  We test all the possible splits – we do not have many contextual features  We choose one split (using a single contextual feature) that maximizes an impurity measure and whose impurity is higher than a threshold abcd Item Split - Intuition and Approach Context-Based Recommender Systems 39
  39. 39.  Each item in the data base is a candidate for splitting  Context defines all possible splits of an item ratings vector  We test all the possible splits – we do not have many contextual features  We choose one split (using a single contextual feature) that maximizes an impurity measure and whose impurity is higher than a threshold abcd Item Split - Intuition and Approach Context-Based Recommender Systems 40
  40. 40. Context-Aware Splitting Approaches
  41. 41. Types of Context
  42. 42. Different Views of Context
  43. 43. Model of Context-Based Recommender Systems
  44. 44. Context-Based Pre Filtering Model
  45. 45. Context-Based Post Filtering Model
  46. 46. Context-Based Contextual Model
  47. 47. ?3 Active user Rating prediction Trust- based Collaborative Filtering Active users’ trusted friends Users tend to receive advice from people they trust, such as Trusted friends who can be defined explicitly by the users or inferred from social networks .
  48. 48. • Global Metrics: computes a single global trust value for every single user (reputation on the network) • Pros: o Based on the whole community opinion • Cons: o Trust is subjective (controversial users) a b d c 1 3 32 3 Metrics of Trust based Recommender
  49. 49. • Local Metrics: predicts (different) trust scores that are personalized from the point of view of every single user • Pros: o More accurate o Attack resistance • Cons: o Ignoring the “wisdom of the crowd” a b d c 1 5 32 ? Metrics of Trust based Recommender
  50. 50. 51 Content-Based Recommender System • In content-based recommendations the system tries to recommend items that matches the User Profile; • The Profile is based on items user has liked in the past or explicit interests that he defines; • A content-based recommender system matches the profile of the item to the user profile to decide on its relevancy to the user;
  51. 51. 52 Read update User Profile New books User Profile Recommender Systems Match recommendation Example of Content Based Recommender
  52. 52. What is the “Content"? • The genre is actually not part of the content of a book • Most CB-recommendation methods originate from Information Retrieval (IR) field: o The item descriptions are usually automatically extracted (important words) o Goal is to find and rank interesting text documents (news articles, web pages) • Here are some examples: o Classical IR-based methods based on keywords o No expert recommendation knowledge involved o User profile (preferences) are rather learned than explicitly elicited
  53. 53. Content Representation • Items stored in a database table
  54. 54. Content Representation • Structured data  Small number of attributes  Each item is described by the same set of attributes  Known set of values that the attributes may have • Straightforward topic to work with  User’s profile contains positive rating for 1001, 1002, 1003  Would the user be interested in say Oscar, French cuisine, table service? • Unstructured data  No attribute names with well-defined values  Need to impose structure on free text before it can be used  Natural language complexity  Same word with different meanings  Different words with same meaning
  55. 55. Term-Frequency - Inverse Document Frequency • Simple keyword representation has its problems o In particular when automatically extracted because • Not every word has similar importance • Longer documents have a higher chance to have an overlap with the user profile • Standard measure: TF-IDF o Encodes text documents as weighted term vector o TF: Measures, how often a term appears (density in a document) • Assuming that important terms appear more often • Normalization has to be done in order to take document length into account o IDF: Aims to reduce the weight of terms that appear in all documents
  56. 56. TF - IDF Weighting • Term frequency tft,d of a term t in a document d • Inverse document frequency idft of a term t • TF*IDF weighting   k dk dt dt n n tf , , ,        t t df N idf log   tdt idftfdtw  ,,
  57. 57. Example TF IDF Representation
  58. 58. User Profiles • User profile consists of two main types of information  A model of the user’s preferences. e.g., a function that for any item predicts the likelihood that the user is interested in that item  User’s interaction history. e.g., items viewed by a user, items purchased by a user, search queries, etc. • “Manual” recommending approaches  Provide “check box” interface that let the users construct their own profiles of interests  A simple database matching process is used to find items that meet the specified criteria and recommend these to users. • Rule-based Recommendation  The system has rules to recommend other products based on user history  Rule to recommend sequel to a book or movie to customers who purchased the previous item in the series  Can capture common reasons for making recommendations

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