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Editor's Notes
- #2 * My topic are recommender engines * We all know them from websites like ...
- #3 ... Amazon (the \"Customers who bought this also bought\" feature) ...
- #4 ... Digg.com (“Diggers Like You” recommend upcoming stories) ...
- #5 ... or last.fm (Music, Events, and Videos matching your music taste)
- #6 * 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
- #7 * 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.
- #8 * 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.
- #9 * 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
- #10 * 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.
- #11 * Now I’m going to talk about the recommendation techniques with their strengths and weaknesses
- #12 * First we have non-personalized recommendations
- #13 * 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
- #14 * 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
- #15 * 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
- #16 * 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
- #17 * 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
- #18 * 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.
- #19 * 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
- #20 * 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
- #21 * 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
- #22 * 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.
- #23 * 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
- #24 * 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
- #25 * In the matrix it looks like this * Items I and I5 have high ratings from the same users, so I5 is recommended
- #26 * 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
- #30 * 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
- #36 * 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
- #37 * 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
- #39 * 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.
- #40 * Of course there is the famous “Customers who bought this also bought” feature shown on every item detail page ...
- #41 .. and also on the shopping cart page.
- #42 * 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.