Prof. Dr. Dr. Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim Recommender Systems for E-Commerce

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1 Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

2 Recommender Systems for E-Commerce Applications Prof. Dr. Dr. Lars Schmidt-Thieme Arwed-Löseke-Stiftungsprofessur für Wirtschaftsinformatik und Maschinelles Lernen Information Systems and Machine Learning Lab (ISMLL) Institute of Economics and Information Systems & Institute of Computer Science University of Hildesheim Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

3 Recommender Systems I. Recommender System Applications II. Modelling vs. Learning Customer Behavior III. Assessing Recommendation Quality IV. Outlook Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

4 Recommender Systems / I. Recommender System Applications Anatomy of an Online-Shop detailview comparison Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

5 Recommender Systems / I. Recommender System Applications Anatomy of an Online-Shop info layer access layer keyword/fulltext search detailview hierarchical catalog comparison Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

6 Recommender Systems / I. Recommender System Applications Anatomy of an Online-Shop info layer access layer keyword/fulltext search detailview hierarchical catalog comparison market basket order system INFORMATION SYSTEM TRANSACTION SYSTEM Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

7 Recommender Systems / I. Recommender System Applications Anatomy of an Online-Shop info layer access layer keyword/fulltext search relevance sorting hierarchical catalog gallery detailview cross links reviews comparison INFORMATION SYSTEM market basket gallery order system TRANSACTION SYSTEM Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

8 Recommender Systems / I. Recommender System Applications Anatomy of an Online-Shop recommender system info layer access layer keyword/fulltext search relevance sorting hierarchical catalog gallery detailview cross links reviews comparison INFORMATION SYSTEM market basket gallery order system TRANSACTION SYSTEM Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

9 Recommender Systems / I. Recommender System Applications Recommender systems are online information systems that recommend s to s (automatic selling), contrary to static lists (special offers, editor s choice, etc.) are personalized and targeted at the individual, use profiles consisting of explicit and implicit ratings, to achieve personalization, are also known as collaborative filtering, relevance feedback etc. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

10 Recommender Systems / I. Recommender System Applications Example I: personalized recommender system (1/3) Figure 1: Personalized recommender system: explicit rating. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

11 Recommender Systems / I. Recommender System Applications Example I: personalized recommender system (23) Figure 2: Personalized recommender system: recommendation list. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

12 Recommender Systems / I. Recommender System Applications Example I: personalized recommender system (3/3) Figure 3: Personalized recommender system: explanation of recommendation. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

13 Recommender Systems / I. Recommender System Applications Example II: anonymous recommender system (1/3) Figure 4: Anonymous recommender system: task description. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

14 Recommender Systems / I. Recommender System Applications Example II: anonymous recommender system (2/3) Figure 5: Anonymous recommender system: recommendation list. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

15 Recommender Systems / I. Recommender System Applications Example II: anonymous recommender system (3/3) Figure 6: Anonymous recommender system: explanation of recommendation. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

16 Recommender Systems I. Recommender System Applications II. Modelling vs. Learning Customer Behavior III. Assessing Recommendation Quality IV. Outlook Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

17 Recommender Systems / II. Modelling vs. Learning Customer Behavior Modelling Customer Choice Behavior Customer choice behavior can be modelled explicitly by a conceptual model Several layers of need concepts mitigate observable preference indicators (such as answers to questions) and properties / components Requires team of domain experts, marketing people, and knowledge engineers. Time consuming and expensive. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

18 Recommender Systems / II. Modelling vs. Learning Customer Behavior Modelling Customer Choice Behavior Product P Product Properties PP1 PP2.. PP k Abstract Customer Needs ACN1 ACN2.. ACN n Customer Needs Questions.. CN1 CN2 CN i Q1 Q2 Q.. l Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

19 Recommender Systems / II. Modelling vs. Learning Customer Behavior Anatomy of an Online-Shop info layer access layer keyword/properties search relevance sorting hierarchical catalog gallery detailview cross links reviews comparison recommender system task spec. INFORMATION SYSTEM market basket gallery order system TRANSACTION SYSTEM Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

20 Recommender Systems / II. Modelling vs. Learning Customer Behavior Anatomy of an Online-Shop info layer access layer keyword/properties search relevance sorting hierarchical catalog gallery detailview cross links reviews recommender system task spec. comparison INFORMATION SYSTEM market basket gallery order system TRANSACTION SYSTEM databases properties hierarchy search index marketing preferences Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

21 Recommender Systems / II. Modelling vs. Learning Customer Behavior Anatomy of an Online-Shop OPERATIVE SYSTEM info layer access layer databases keyword/properties search relevance sorting properties hierarchical catalog gallery detailview cross links reviews hierarchy search index recommender system task spec. comparison INFORMATION SYSTEM marketing system log market basket gallery order system TRANSACTION SYSTEM preferences ANALYTICAL S. databases preference indicators Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

22 recommender Recommender Systems / II. Modelling vs. Learning Customer Behavior system Anatomy of an Online-Shop OPERATIVE SYSTEM ANALYTICAL S. info layer access layer databases databases keyword/properties search relevance sorting properties hierarchical catalog gallery detailview cross links reviews hierarchy search index shop data warehouse recommender system task spec. comparison INFORMATION SYSTEM marketing system log preference indicators market basket gallery order system TRANSACTION SYSTEM preferences LEARNING Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

23 Recommender Systems / II. Modelling vs. Learning Customer Behavior Learning Preferences Automatically Preferences can be learnt automatically by classifiers. Predictions based on β short questionnaire (4 7 closed-form questions). α θ Z Y buying behavior, i.e., past purchases, or explicit ratings (autocorrelation models). Figure 1: Aspect Model Figure 1: Aspect Model β Figure 2: LDA Model M N β R 1 β information seeking behavior (implicit feedback). θ Z R 2 R 1 Z 1 R 1 Different models are in use: θ N collaborative filtering (nearest neighbor classifier). Z R M R 2 α θ Z 2 R M Figure 3: Multinomial Mixture Model Z M N R 2 R M N Bayesian networks. Figure 3: Multinomial Mixture Model P (Z = z) for that Figure user. 4: URP ThisModel gives URP mu at the user level than the multinomial mixt be generalized to use any continuous distrib P (Z = z) for that user. This gives URP the much Dirichlet more leads powerful to efficient generative prediction semantics eq at the user level than the multinomial the mixture LDA model. model consists As with of LDA, an item URP variable could be generalized to use any continuous distribution at any point. on the simplex, but in this case the Dirichlet leads to efficient prediction equations. Note that the bottom level of Compound classification models (1-vs-1 Support Vector Machines). Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

24 Recommender Systems I. Recommender System Applications II. Modelling vs. Learning Customer Behavior III. Assessing Recommendation Quality IV. Outlook Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

25 DIE DER drehtdemkäuferi,hrenrückenzu Recommender Systems / III. Assessing Recommendation Quality s ist ganz einfach. Ich möchte ein mittlerweile vergriffen. Seither bin ich verzweifelt gutes Buch lesen.wieder einmal. auf der Suche nach Werken, die jenen das WasEinfach ein gutes Buch, das ich ser reichen können, die ebenso. viel Spaß und in meine Tasche packe, mit dem Freude bereiten. Nur selten wurde ich fündig. ich an den Flussspaziere,mich ir- Denis Johnsons Schon tot, Steve Tesichs Abspann gendwohin setze, in die herbstli- oder Martin Amis' Gierig kamen dem nahe. Aber che Sonne, die Schwane ignorie-, nur nahe. Philip Roth' Sabbaths Theater war eine rend, die Passanten vergessend, die ganze Welt Offenbarung (obwohl ich anfangs skeptischwar, auch, und dann ein bisschen lese, in dem Buch, dass mich das Sexuallebenalter Menschen intedem guten. Aber ich habe kein gutes Buch zur ressieren könnte - es tat's). Das ist aber auch schon eine Weile her. In letzter Zeit kam mir wehand. Es fehlt mir. Ein.gutes Buch, damit meine i<;:hbelletristik. nig in die Finger, was mich auch nur irgendwie Einen Roman. Eine Geschichte, in der man ein glücklich tnachen konnte. Die letzten Röths paar Tagewohnen kann, vielleichtsogarüber eine langweilten mich auf extremeart, trotz der imwoche; denn manchmal sind Bücher wie Betten: mer jubelnden Kritik. BeiDer menschlichemakel Man möchte die Geschichten nicht mehr verlas- stieg ich auf Seite54 aus. Ich war zeitweiligsoversen. Sicherlichgibt esauch gute Sachbücher,über zweifelt, dass ich versuchte, mit Jugendlieben Tontauben-Schießen, über 1000 tolle Steuer- wieder anzubändeln, mitautoren, die mich einst tricks oder warum Männer zu viel reden und mit dem Lesevirusinfizierthatten. Etwa mit neufrauen manchmal auch. Das gute Buch aber ist en Werken von Irving (Die viertehand, eine reider Roman. Eine Reisedarin am besten, ein Jahr- ne Katastrophe, wie aus dem Textcomputer, erhundert oder mehr überspannend oder auch nur bärmlich, unlesbar), Boyle (seit Riven Rock kann ein paar Wochen. Das beste Buch, das ich je ge- ich seinebücher nicht mehr lesen,auch,nichtsein lesen habe, das war Mordecai Richlers Solomon letztes,drop City:Am Anfang noch ganz hübsch, Gurskywarhier.Ein Glücksfall,wie auch die zwei dann aber sieht man bald das Flipchartin Boyles anderen Richler, die ich sogleichnach dem ersten Küche, auf dem er den Verlaufder Geschichte in verschlang: W'ieBarneyessiehtund Der Traumdes Diagrammen festgehalten hat und steigt erschrejakob Hersch;Selten war'ich so glücklich wie in ckend kalt aus, verlässtdas Buch wie eine nie geder Zeit, die ich in diesen Büchern verbringen liebte Beziehung, sagen wir auf Seite 254) oder durfte. Von Richler kann man nichts mehr er~ Murakami (ist wie beim Sushi: Hat man irgendwarten. Er starb vor zweijahren. Leider. wann ja auch mal genug von). Diese Bücher von Richler aber, die habe ich Ich las Bücher nochmals, aber ein Buch schon vor drei Jahren gelesen. Eines der drei ist nochmals zu lesen, das kann niemals das gleiche top: Die ZEIT, October 9, 2003, p. 57 (Max Küng, Ein Buch! Ein gutes! Bitte!) right: Amazon, October 16, 2003 Vergnügen sein, wie ein Buch das erste Mal, sen. Und hab ich schon von Denis Johnsons J tem Buch erzählt? Fiskadoro heißt es. Denis J son, dessen Schon tot - wie bereits erwähnt. so sehr mochte und das zu jenen Dingen g! te, die ich meinen Freunden eindringlid Herz legte wie auch andere Dinge, zum Bei n mit dem Fahrradfahren anzufangen oder. Wandern oder dem Fliegenfischen. Meine ( als ich die Ankündigung las, dass der neue J son bald erscheinen würde, da war ich rich\ hend aufgeregt. Seit ich als Kind bei einem sandhandel ein Skateboard bestellte und d<ll fach nicht kam und nicht kam, habe ie!,nichts mehr so sehnlich gewartet wie auf ~ Buch. Ich rief sogar beim Verlag an und bes ein Leseexemplar. Und dann kam es. Wär kein Fan von Johnson, ich wäre wohl kaum den Klappentext hinausgekommen:»im 21..,._"-",,---',""-,,",,"c_""'D-~'-"~o.&..'~~~~;""'-""'-"'-'~"'.'.~,_,<~",,"'"""--"'-~~-""_c:...'...,...,..,.t..~-,-~",~."",.~ Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

26 Recommender Systems / III. Assessing Recommendation Quality Experimental Results in the Lab recall by probability of competitive items (top 10 ranks hit) recall pairwise SVMs itembased c.f. most popular #probability of competitive items 2,000,000 pairwise classifiers on a total of 200,000,000 cases. computation time for a single experiment 20d. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

27 Recommender Systems / III. Assessing Recommendation Quality Cooperation With Mentasys GmbH 1. Anonymous recommender systems (Sales Assistant) applied at many German and European online retailers (Karstadt, Otto, etc.) as well as on ebay. empirically measured up to 20% increase in volume of sales for some segments (Avitos, digital cameras). 2. Information integration: identification of offers for price comparison platforms. A I A I B H B H C E G C E G F F Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

28 Recommender Systems / III. Assessing Recommendation Quality Example III: Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

29 Recommender Systems I. Recommender System Applications II. Modelling vs. Learning Customer Behavior III. Assessing Recommendation Quality IV. Outlook Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

30 Recommender Systems / IV. Outlook Recommenders in Finance I hybrid model consisting of nodes, usually shown as squares, represent the choices available to the decision-maker. Utility nodes, usually of diamond or flattened hexagon shape, represent the usefulness of the consequences of the decisions measured on a decision numerical utility tree scale. The arcs in the graph have different meanings based on their destinations. Dependency arcs are the arcs that point to utility or chance nodes representing probability or functional dependence. Informational arcs are the arcs that point to the decision nodes implying that the pointing nodes will be known trained to the decision-maker data for before the decision is made. influence diagram (Bayesian network) S & P 500 CompuStat database backtesting split: evaluated on data for Decision tree recommended 132 out of 500 companies offigure the3. S Influence & P index diagram module Decision tree has the following characteristics: 1. It is non-parametric and therefore independent of the distribution of class signature, 2. It can handle both continuous and nominal variables, 3. It generates portfolio number of companies 1 year total return interpretable classification rules, and 4. It is fast to train S & and Pis often 500as accurate as, sometimes more 500accurate than, 24% many other classifiers. Vanguard A decision 500 tree reaches its decision 500 by performing a 32% Tseng sequence 2004 of tests. Each internal node 132in the tree 42% corresponds to a test of the value of one of the attributes. The branches from the node are labeled with the possible Chiu-Che outcome Tseng values (2004): of the test. Portfolio Each leaf Management node contains using the Hybrid Recommender Systems, output value to be return when it is reached. This is a very Proc. of the 2004 IEEE International Conference on e-technology, e-commerce and e-service nature process for a human decision maker, in fact a lot of (EEE 2004). manuals are written in this fashion. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17 Figure 4. Decision tree module (partial tree).

31 Recommender Systems / IV. Outlook Recommenders in Finance II Figure 2: Example user interface. Alexander Felfernig, Klaus Isak, Gerold Kreutler, Thomas Kruggel and Erich Teppan (2007): jor requirement. The architecture depicted in can be found, a set of possible repair actions is proposed. Knowledge-based Interactive Selling of Fiancial Services, ure 1 shows the integration of advisory services into the Having completed this initial analysis phase (the result is a tware environment Information (CRM Systems System and and E-Business Mainframe Management Enviment) Prof. Dr. of Dr. a financial Lars Schmidt-Thieme, service provider. Information The Systems CRMand system Machine Learning ), Lab (ISMLL), one University or more of Hildesheim detailed advisory sessions can be 5/2, set of p. topics/selling opportunities to be discussed with the as front-end for sales representatives. It can be a local started (e.g. an investment advisor is used to identify an in- Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17

32 Recommender Systems / IV. Outlook Conclusion Recommender systems are one of the key technologies for e-commerce providing cross and up selling. Recommender systems can be based on different types of preference indicators such as buying behavior or short questionnairs (depending on the application). Recommender systems can either be modeled conceptually by domain experts or learnt automatically with methods from Machine Learning. Recommender systems can be anonymous and based on task profiles for first-time s (standard technology) or deeply personalized for repeat buyers (state-of-the-art technology). While recommender systems are widely used by now, there are still many exciting open research problems and challenges. Recommender Systems for E-Commerce Applications, Arbeitskreis Informationstechnologie, Hildesheim, /17