RWTH Aachen
Fraunhofer FIT
E-Commerce Seminar WT 08/09




     Recommender Engines
                        Thomas Hess (2...
Recommender Engines


• What are Recommender Engines?
• Recommendation Techniques
• Examples in E-Commerce
What are
Recommender Engines?
• Personalized Information Agents
• Predict items a user may be interested in
• Engines use ...
What are
Recommender Engines?
Role of Recommender
Engines in E-Commerce
• Integral part of many e-businesses
• Are an unique feature of e-businesses
• W...
Benefits provided by
Recommender Engines
• Make users purchase more items
• Help gaining customer loyalty
• Build a “value-...
Recommender Engines


• What are Recommender Engines?
• Recommendation Techniques
• Examples in E-Commerce
Demographic
     Recommendation
• Use information about the users
  (demographics)
• Find users with similar features
• Re...
Demographic
Recommendation
Demographic
Recommendation
Demographic
        Recommendation
•   Advantages:
    •   No user-item ratings needed - new users can
        immediately...
Content-based
     Recommendation
• Use information about the items (e.g.
  keywords, genres)
• Find items similar to the ...
Content-based
Recommendation
Content-based
Recommendation
Content-based
     Recommendation
• Works well if items can be properly
  represented as a set of features
• Problems:
 • ...
User-based
Collaborative Filtering

• Use user-item ratings matrix
• Make user-to-user correlations
• Find highly correlat...
User-based
Collaborative Filtering
User-based
Collaborative Filtering
User-based
Collaborative Filtering
User-based
Collaborative Filtering
•   Advantage:
    •   No knowledge about item features needed
•   Problems:
    •   Ne...
Item-based
Collaborative Filtering
• Use user-item ratings matrix
• Make item-to-item correlations
• Find items that are h...
Item-based
Collaborative Filtering
Item-based
Collaborative Filtering
Item-based
Collaborative Filtering
Item-based
Collaborative Filtering
•   Advantages:
    •   No knowledge about item features needed
    •   Better scalabil...
Hybrid Approaches
• Combine collaborative and demographic/
  content-based techniques
• Collaborative filtering systems ach...
Recommender Engines


• What are Recommender Engines?
• Recommendation Techniques
• Examples in E-Commerce
Amazon.com
•   Recommender engine based on item-based
    collaborative filtering
•   A item-to-item matrix is calculated o...
Digg.com
•   Recommender engine is based on user-based
    collaborative filtering
•   Correlation coefficient is calculated...
Conclusion
•   Recommender engines are a powerful
    technology for personalization
•   Provide benefits for businesses an...
Thank You!
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
Recommender Engines
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Recommender Engines

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  • * My topic are recommender engines
    * We all know them from websites like ...
  • ... Amazon (the \"Customers who bought this also bought\" feature) ...
  • ... Digg.com (“Diggers Like You” recommend upcoming stories) ...
  • ... or last.fm (Music, Events, and Videos matching your music taste)
  • * First I’m talking about what recommender engines are in general and e-commerce
    * Second I will talk about the different techniques used by recommender engines
    * And finally I’m going to show some examples of recommender engines in e-commerce
  • * Recommender engines are Personalized Information Agents, which means, they provide personalized information to individual users
    * Particularly they recommend items to users by predicting which items out of a large pool a user may be interested in
    * Recommender engines use a variety of techniques that use different knowledge sources for their predictions
    * These knowledge sources can be information about the users, about the items and information about users’ preferences for items. These are called ratings.
  • * Here we have the concept again in an illustration
    * We have a pool of items and a group of users
    * The recommender engine uses knowledge about the users, the items, and the user-item preferences
    * Based on this knowledge it recommends a specific item to a specific user


    * The user-item preferences are often called user-item ratings and can be derived from many different sources like the purchase history, explicit item ratings or preference questionnaires.
  • * So what role do recommender engines play in e-commerce?

    * Recommender engines are used by more and more e-commerce businesses
    * They are a tool for to personalize websites for customers
    * Personalization is something only e-businesses can do in contrast to real stores
    * E-Businesses have no storage space constrains, in contrast to real world stores
    * Therefore they can have very large catalogs of items
    * But these are unable to be looked through manually, which makes personalization a necessity
  • * What benefits do e-businesses have from using recommender engines?

    * Recommender Engines present customers with items that are interesting for them, but that they didn’t plan to buy
    * This makes them to buy more items, which increases the sales of the e-business
    * These unplanned purchases are common in real world stores but don’t happen that often in e-businesses

    * Engines make it easier and faster to find new items, which makes the customers come back more often. Customer loyalty is increased.

    * And the often customers come back and the more they purchase, the more the recommendation system knows about them and their preferences.
    * The accuracy of the recommendations increases which increases the value of the website for the user.

    * Last but not least E-businesses can promote specific items through the recommendation system.
  • * Now I’m going to talk about the recommendation techniques with their strengths and weaknesses
  • * First we have non-personalized recommendations
  • * First we have demographic recommendations

    * As said before the techniques are classification based on which knowledge sources they use
    * Demographic recommendation uses the knowledge about the users
    * This are features like age, gender, profession, income, or location

    * To make recommendation the system first looks for users with similar features to the one it wants to find recommendations for
    * And then recommends items that are preferred by these similar users
  • * Here is the process again in an illustration
    * We have three users and three items
    * User 2 likes Item A and User 3 likes Item C
    * User 1 and User 3 have similar demographic features
    * So the system recommends Item C to User 1

  • * The advantages of demographic recommendation are:
    * The technique does not need rating data for the user it recommends items to
    * So a new user without any ratings can immediately get recommendations as long as he has given information about himself
    * The techniques also doesn’t need to know anything about the items the system deals with

    * The drawbacks are:
    * Users generally don’t like to give away information about themselves, but this is the only knowledge source the system has
    * Demographic data is too crude to make good recommendations. The system will make generalisations that are problematic especially when the system deals with cultural items like books, music, or movies.
    * Not all 30 year old females like the same movies.
    * Users with unusual taste will get no good recommendations
  • * The next technique are content-based recommendations
    * This technique uses information about the items
    * This are keywords or genres. For movies for example this could be year, title, director, actors and so on.
    * The system first looks at the items a user has preferred in the past
    * And then searches the item database for similar items that have the same features
    * From these search results the engine then recommends the items that have the highest similarity

  • * Here is how it works in an illustration
    * We have a user and three items
    * Items A and C have the same features
    * So the engine recommends Item C to the user
  • * This technique works well if the items can be properly represented as a set of features.
    * If the items or the item descriptions can be analysed with automatic content analysis the features can be extracted automatically.
    * But if this is not possible it has to be done manually, which takes a lot of resources.

    * The technique has problems if new users have rated none or only a few items. Because then the system doesn’t know what items to search for.
  • * The next techniques are collaborative filtering techniques.
    * These techniques use the user-item rating data matrix as knowledge source.

    * First we look at user-based collaborative filtering.
    * This technique makes correlations between users based on their item preferences.

    * The system first finds highly correlated users based on their rating profiles, that means users that have the shared item preferences
    * Then it recommends items that the highly correlated users also preferred
  • * In the illustration it looks like this
    * We have three users, and three items
    * User 1 likes items A and B
    * User 2 likes item C
    * and User 3 likes all three items
    * So User 1 and 3 share their preference for items A and B and have therefore a high correlation
    * So the recommender system recommends item C to User 1 as there is a high probability User 1 will like it
  • * In the user-item rating matrix this looks like this
    * We have 6 users, 5 items and ratings from 1-10
    * User U3 has very similar ratings to User A, so there is a high correlation
    * As user U3 also gives item I5 a high rating, we can recommend this item to User a
  • * The advantage of all collaborative filtering techniques is, that they don’t need to know anything about the items the system deals with, just like demographic recommendation

    * The problems are:
    * We have the new user cold start problem that we also had with the content-based recommendation
    * We also have a cold start problem for new items, which means that new items that have none or only a few ratings cannot be recommended easily
    * Sparse data in the rating matrix is a problem, as collaborative filtering techniques depend on an overlap in ratings across users. If few users have rated the same items the quality of the recommendations gets bad.
    * User-based collaborative filtering also does not scale well for large data sets. The user-dimension of the matrix usually becomes much larger than the item-dimension, which makes drawing of user-to-user correlations very expensive.
  • * The next technique is item-based collaborative filtering
    * Here we don’t draw correlations between users but between items

    * The algorithm looks at an item and then finds other items that are preferred by the same users
    * So it builds item-to-item correlations based on the shared appreciation of users
    * The highly correlated items are then recommended

  • * In the diagram it looks like this
    * We again have three users and three items
    * User 1 likes Item A
    * User 2 and User 3 like items A and C
    * So the Items A and C are correlated as they are preferred by the same users
    * So the engine recommends item C to User 1
  • * In the matrix it looks like this
    * Items I and I5 have high ratings from the same users, so I5 is recommended
  • * The advantages of this technique are:
    * No knowledge about the items is needed as in all collaborative filtering approaches
    * The approach scales better than the user-based approach, as the number of items is usually smaller than the number of users, and items are easier to categorize.
    * Because of this the sparsity problem is also reduced.

    * But the approach also has the cold start problems for new users and new items



  • * The last approach I’m going to present are hybrid recommender systems
    * This is not a new technique but rather a combination of techniques
    * As we have seen all of the techniques have benefits and drawbacks
    * Hybrid approaches try to combine complementary techniques so that the drawbacks will be neutralized
    * This works especially for the combination of collaborative filtering techniques with demographic or content-based techniques
    * Collaborative filtering techniques achieve a high prediction accuracy but have problems when the available rating data is limited
    * Here demographic and content-based can help out because they don’t rely on rating data





  • * Concluding I’m going to show two examples of websites that use recommender engines
    * What techniques do they use and how are the recommendations used on the websites
  • * Recommender engines are developed and run on the one hand by independent technology vendors
    * Also many e-businesses have their own engines developed
    * ChoiceStream is one of the leading independent vendors

  • * The first one is amazon.com
    * Amazon is maybe the most famous example for an e-business utilizing a recommender engine
    * They use the recommendation very extensively to personalize their website

    * The recommender engine is based on item-based collaborative filtering
    * Their system consists out of an online and an offline component
    * The offline component calculates a item-to-item matrix with the correlation coefficients
    * It does this by using a similarity function that gets applied to pairs of items that customers tend to purchase together

    * The online component then only has to lookup similar items in this matrix when recommendations are needed

    * Through this separation Amazon is able to deal with their huge data set of several million catalog items and tens of millions of users.
    * The expensive calculations are performed offline and the online component scales independent on catalog size and number of users. It only depends on the number of items a user has purchased and rated.

    * Now let’s see how the recommendation engine is used on the website.
  • * Of course there is the famous “Customers who bought this also bought” feature shown on every item detail page ...
  • .. and also on the shopping cart page.
  • * Then there is the “Your Recommendations” page, that shows all the recommendations for you.
    * You can mark recommendations as already owned or not interested in and also rate them in order to influence your recommendations.
    * It is also shown why an item is recommended, that is which purchase is correlated to the recommended item.


  • Recommender Engines

    1. 1. RWTH Aachen Fraunhofer FIT E-Commerce Seminar WT 08/09 Recommender Engines Thomas Hess (289222) January 29, 2009
    2. 2. Recommender Engines • What are Recommender Engines? • Recommendation Techniques • Examples in E-Commerce
    3. 3. What are Recommender Engines? • Personalized Information Agents • Predict items a user may be interested in • Engines use different techniques utilizing different knowledge sources • Knowledge can be derived from user features, item features and user-item ratings
    4. 4. What are Recommender Engines?
    5. 5. Role of Recommender Engines in E-Commerce • Integral part of many e-businesses • Are an unique feature of e-businesses • Websites can track customer’s behaviours • E-businesses can offer huge stocks, that make personalization necessary • Recommender engines allow personalization
    6. 6. Benefits provided by Recommender Engines • Make users purchase more items • Help gaining customer loyalty • Build a “value-added relationship” between the e-business and the customer • Promote older and low-demand items
    7. 7. Recommender Engines • What are Recommender Engines? • Recommendation Techniques • Examples in E-Commerce
    8. 8. Demographic Recommendation • Use information about the users (demographics) • Find users with similar features • Recommend items that are preferred by similar users
    9. 9. Demographic Recommendation
    10. 10. Demographic Recommendation
    11. 11. Demographic Recommendation • Advantages: • No user-item ratings needed - new users can immediately get recommendations • No knowledge about item features needed • Problems: • Users have privacy concerns about disclosing information about themselves • Demographic data is too crude for highly personalized recommendations
    12. 12. Content-based Recommendation • Use information about the items (e.g. keywords, genres) • Find items similar to the items preferred in the past • Recommend the items with the highest similarity
    13. 13. Content-based Recommendation
    14. 14. Content-based Recommendation
    15. 15. Content-based Recommendation • Works well if items can be properly represented as a set of features • Problems: • Content analysis is necessary • New user cold start problem: users with little or no user-item ratings are difficult to categorize
    16. 16. User-based Collaborative Filtering • Use user-item ratings matrix • Make user-to-user correlations • Find highly correlated users • Recommend items preferred by those users
    17. 17. User-based Collaborative Filtering
    18. 18. User-based Collaborative Filtering
    19. 19. User-based Collaborative Filtering
    20. 20. User-based Collaborative Filtering • Advantage: • No knowledge about item features needed • Problems: • New user cold start problem • New item cold start problem: items with few ratings cannot easily be recommended • Sparsity problem: depends on overlap in ratings across users - has difficulties when space of ratings is sparse • Does not scale well for large data sets
    21. 21. Item-based Collaborative Filtering • Use user-item ratings matrix • Make item-to-item correlations • Find items that are highly correlated with the known preferred ones • Recommend items with highest correlation
    22. 22. Item-based Collaborative Filtering
    23. 23. Item-based Collaborative Filtering
    24. 24. Item-based Collaborative Filtering
    25. 25. Item-based Collaborative Filtering • Advantages: • No knowledge about item features needed • Better scalability, because correlations between limited number of items instead of very large number of users • Reduced sparsity problem • Problems: • New user cold start problem • New item cold start problem
    26. 26. Hybrid Approaches • Combine collaborative and demographic/ content-based techniques • Collaborative filtering systems achieve better predictions but have cold start and sparsity problems • Demographic/content-based systems work without rating data and can therefore compensate these drawbacks
    27. 27. Recommender Engines • What are Recommender Engines? • Recommendation Techniques • Examples in E-Commerce
    28. 28. Amazon.com • Recommender engine based on item-based collaborative filtering • A item-to-item matrix is calculated offline through a similarity function using item pairs that customers tend to purchase together • Thus the online lookup of similar items for recommendations is very fast and scales independent of catalog size and number of customers
    29. 29. Digg.com • Recommender engine is based on user-based collaborative filtering • Correlation coefficient is calculated between users than “dugg” the same stories • Upcoming stories that have been “dugg” by highly correlated users are recommended • Engine uses a custom graph-database and runs in real-time without prediction model and batch processing
    30. 30. Conclusion • Recommender engines are a powerful technology for personalization • Provide benefits for businesses and consumers • Item-based collaborative filtering in a hybrid system with content-based recommendation is state of the art • But which technique works best always depends on the concrete use case
    31. 31. Thank You!

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