A Personalized Recommendation System Based on PRML for E-Commerce

1. A Personalized Recommendation System Based on PRML for E-Commerce Young Ji Kim, Hyeon Jeong Mun, Jae Young Lee and Yong Tae Woo Dept. of Computer Sciences, Kosin University, Korea yjkim@hibrain.net

2. Personalization • What’s Personalization? • The process of customizing the contents and structure of a web site to the specific and individual needs of each user taking advantage of the user’s behavior patterns. • Why need Personalization? • Technique to maintain closed relationships with clients. • analyzing clients preferences. • providing differentiated service to preferred clients for Internet based applications. • Important role in a one-to-one marketing strategy to enhance both customer satisfaction and profits on an E-commerce site.

3. Personalization • What is the need for personalization? • Need to know client’s preferences. • What did clients buy? • What did clients want or like? • What things will the client be interested in? • Steps to personalization. • Collect user’s behavior. • Analyze user’s behavior from collected data. • Predict user’s behavior using analyzed results. • Recommend things which client will be interested in.

4. Personalized Recommendation System • What’s a personalized recommendation system? • Analyze user’s behavioral patterns and recommend new products that best match the individual user’s preferences. • Existing recommendation techniques • Rule-based filtering technique • Use demographic information • Collaborative filtering technique • Use other user’s rating value with similar preference • Content-based filtering technique • Compare user profile and product description • Item-based filtering technique • Analyze association among products

5. Personalized Recommendation System • Problems of the existing techniques • Some users are concerned about privacy issues • Do not enter personal information. • Enter incorrect information. • Not easy to dynamically incorporate time-varying aspects of user preference using on existing log file. • Existing log file does not contain enough personal information. • Existing methods are tailored to particular applications. • Lack ability to analyze user behavior patterns. • Lack ability to dynamically generate and recommend web contents.

6. Proposed System • Proposed system • Propose a new personalized recommendation technique based on PRML. • First, we make each user’s PRML instance. • User’s behaviors are collected from XML-based web sites. • Save them as PRML instance. • Second, we build each user’s profile. • Analyze each user’s PRML instance. • Make each user’s profile using them. • Third, we recommend the products with Top-N similarities. • Personalized recommendations are made by comparing the similarity between the information about new products and user’s profile.

7. Proposed System

8. Personal Information Collection System • What’s PICS(Personal Information Collection System)? • Collect user’s behavioral patterns while a user is connected. • When the user connect. • Where the user connect. • What the user do. • click, read and scrap contents, use shopping cart, purchase, etc. • Save it as PRML instances. • Existing method to collect user’s behavior • Need to extract individual user's behavior patterns from mass web log. • Various web log formats such as CLF(Common Log Format), IIS, W3C Ext. have been used in different web servers to record log information.

9. Personal Information Collection System • Existing method to collect user’s behavior

10. Personal Information Collection System • Existing method to collect user’s behavior • Need to preprocess step such as referred in previous section. • Use different log formats and need to remove unnecessary data such as images or scripts. • Difficult to extract session information to identify an individual user. • Difficult to collect user’s behaviors in real time. • Proposed PICS • Implement to collect the personalized information from individual client's behaviors in real time. • Save personalized information as PRML instances.

11. Personal Information Collection System • Configuration of personal information collection system

12. USER USER User Request/ Server Response CBR-Based Feature Information Implicit rating Information PRML for Personalized Services • What’s PRML? • Personalized Recommendation Markup Language. • To efficiently store and manage individual client’s behaviors. • Conceptual diagram of PRML schema PRML  1…m  User Identification Information 0…m 1…m 0…m

13. User Session Management Module • Purpose • To effectively identify and manage user information. • What does it do? • An agent at the server side collects user access information from each user session. • User ID, session ID, IP address, URL, server status and etc. • Convert user access information to PRML instance. • PRML instance is summarized into user identification information and log information. • Save the PRML instance in XML database.

14. User Session Management Module • Schema structure of personal identification information section in PRML

15. User Session Management Module • Example of personalized identification information section in PRML instance

16. Implicit Rating Information Collection Module • Purpose • Implicitly collect rating information from XML-based web sites utilizing hierarchical characteristics of XML documents. • Preparation • Elements in the XML documents are assigned different weights based on their importance in the documents. • Store these weights in the element weight database. • What does it do? • When a user visits a web site, the module collects the XML elements in the XML contents which the user accessed. • Save them as PRML instance.

17. Implicit Rating Information Collection Module • Configuration of implicit rating collection technique • Schema of implicit rating information collection section

18. Experimental XML document • XML schema structure of faculty contents

19. Experimental Element Weight Database • Element weight database • In the element weight database, each element has a level weight and element weight. • The level weight of an element. • Determine by its position in the hierarchy of the XML documents. • The element weight of an element. • Reflect the importance of XML documents. • An experimental element weight database

20. Implicit Rating Information Module

21. CBR feature Information Collection Module • Purpose • Collect CBR feature information to extract user’s preference on web site contents. • Preparation • Select feature elements. • Some elements in an XML document are considered important characteristics. • Store them in the characteristics of XML document database. • What does it do? • When a user accesses XML document, the feature information in the XML document is collected. • Save it as PRML instance along with the user’s implicit rating information.

22. CBR feature Information Collection Module • Configuration of CBR feature collection technique • Schema structure of CBR feature collection section

23. CBR feature Information Collection Module

24. Proposed Personalized Recommendation System • Personalized Recommendation System • Use a CBR-based learning technique. • Create user profile based on the PRML instance and save in the user profile database. • Compute the similarity between the user profile and each new product. • Recommend to the user the new products with Top-Nsimilarities.

25. Personalized Rating Information Calculation Module Element weightDatabase Proposed Personalized Recommendation System • Configuration of proposed system using CBR technique

26. Personalized Rating Information Calculation Module • Purpose • Compute user’s preference of each contents a user accessed. • Use implicit rating information collection section in the PRML instance and element weight database. • Steps to calculate implicit rating information • Group all the elements by content’s id. • all the elements collected by the implicit rating information collection module are divided into groups based on their contents. • Retrieve element weights and level weights from the element weight database. • Compute rating information of the each contents.

27. Personalized Rating Information Calculation Module • Rating information of the content • V is the set of elements in the XML content the user accessed. • le is the level weight of the element e. • ke is the element weight of e. • Rcis the implicit rating information.

28. CBR-based Learning technique • Traditional case-based reasoning system • When a new problem appears, the system retrieves the most similar case, reuses the case to solve the problem. • Revises the proposed solution if necessary, and retains the new solution as a part of a new case. • Proposed the CBR-based Learning technique • Make users profile analyzing user’s behavior patterns. • Suggest the recommendation of the most similar ones using the past preference information stored in the user profile. • Update the user profile for learning the new case.

29. User Profile Management Module • Select contents • Select contents whose implicit rating value(Rc) is high. • Build user profile using CBR feature information refer to selected contents. • User profile • P = (u, A, R, D) • u is a user ID. • A is the set of attributes in the web contents. • R is a set of intra-attribute weights. • D is a set of inter-attribute weights.

30. User Profile Management Module • Intra-attribute weights • The intra-attribute weights R of Ai is {ri1, ri2, ···, rim}. • kij is the number of times aijis accessed. • rijrepresents how much a user prefers the attribute value aij to other attribute values. i = 1, 2, ···,n, and j = 1, 2, ···, m.

31. User Profile Management Module • Intra-attribute weights rij? Compute rij of A1(Major)

32. User Profile Management Module • Inter-attribute weights • The inter-attribute weights D of A is {d1, d2, ···, dn}. • each di represents how much Ai is preferred by the user. • If di is large, • the attribute Ai is more important to the user than other attributes.

33. User Profile Management Module • Inter-attribute weights di? • d1 of Major(A1) = 0.7 – (1/3) = 0.4 • d2of Position(A2) =0.4 – (1/3) = 0.1 • d3 of Location(A3) = 0.8 – (1/2) = 0.3 • each di of Ai(Attribute)

34. Contents Recommendation Module • Contents Recommendation Module • Analyze individual user’s behavioral pattern to generate recommendation for the user. • Use nearest-neighbor approach to compute the similarities between the attributes of user profile(P) and new products(I). • To compute similarity • aij is the attribute value of Ai in P • a’ijis that of I • if aij = a’ij , f (aij, a’ij) returns 1 and otherwise, 0.

35. Experimental Results • Experiment • Experimental content • XML contents of a faculty position recruiting web site. • Number of User • 824 person. • Accessed contents • 1,144 XML faculty contents. • New contents • 1,484 faculty contents.

36. Experiment for Personal Information Collection System • PRML instance

37. Experiment for Proposed Recommendation System • User profile

38. Experiment for Proposed Recommendation System • Experimental Results of recommendation • Use MAE(Mean Absolute Error) and ROC(Receiver Operating Characteristic)

39. Conclusion • Proposed System • Personalized recommendation system • Use the PRML approach. • Define the inter-attribute weights and intra-attribute weights. • Build user profile based on the behavioral patterns of a user. • Recommend the products with Top-N similarities. • Future work • Research a Personalized recommendation system using ontology. • Research User Ontology extending the proposed user profile. • Research Domain Ontology to represent content’s feature. • Research Log Ontology to represent user’s behavior patterns.