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Changwon Nat i Univ. ISIE 2001 SOFSEM06 A Personalized Recommendation System Based on PRML for E-Commerce Young Ji Kim, Hyeon Jeong Mun, Jae Young Lee.

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Presentation on theme: "Changwon Nat i Univ. ISIE 2001 SOFSEM06 A Personalized Recommendation System Based on PRML for E-Commerce Young Ji Kim, Hyeon Jeong Mun, Jae Young Lee."— Presentation transcript:

1 Changwon Nat i Univ. ISIE 2001 SOFSEM06 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 SOFSEM06 1 Personalization Whats 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 users 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 SOFSEM06 2 Personalization What is the need for personalization? –Need to know clients preferences. What did clients buy? What did clients want or like? What things will the client be interested in? –Steps to personalization. Collect users behavior. Analyze users behavior from collected data. Predict users behavior using analyzed results. Recommend things which client will be interested in.

4 SOFSEM06 3 Personalized Recommendation System Whats a personalized recommendation system? –Analyze users behavioral patterns and recommend new products that best match the individual users preferences. Existing recommendation techniques –Rule-based filtering technique Use demographic information –Collaborative filtering technique Use other users rating value with similar preference –Content-based filtering technique Compare user profile and product description –Item-based filtering technique Analyze association among products

5 SOFSEM06 4 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 SOFSEM06 5 Proposed System Proposed system –Propose a new personalized recommendation technique based on PRML. –First, we make each users PRML instance. Users behaviors are collected from XML-based web sites. Save them as PRML instance. –Second, we build each users profile. Analyze each users PRML instance. Make each users 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 users profile.

7 SOFSEM06 6 Proposed System

8 SOFSEM06 7 Personal Information Collection System Whats PICS(Personal Information Collection System)? –Collect users 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 users 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 SOFSEM06 8 Personal Information Collection System Existing method to collect users behavior

10 SOFSEM06 9 Personal Information Collection System Existing method to collect users 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 users 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 SOFSEM06 10 Personal Information Collection System Configuration of personal information collection system

12 SOFSEM06 11 PRML for Personalized Services Whats PRML? –Personalized Recommendation Markup Language. –To efficiently store and manage individual clients behaviors. Conceptual diagram of PRML schema PRML User Identification Information User Identification Information USER CBR-Based Feature Information User Request/ Server Response User Request/ Server Response 1…m1…m 1…m1…m 0…m0…m Implicit rating Information 0…m0…m

13 SOFSEM06 12 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 SOFSEM06 13 User Session Management Module Schema structure of personal identification information section in PRML

15 SOFSEM06 14 User Session Management Module Example of personalized identification information section in PRML instance http://www.w3.org/2001/XMLSchema-instance ………………………….. …………………. ………….

16 SOFSEM06 15 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 SOFSEM06 16 Implicit Rating Information Collection Module Configuration of implicit rating collection technique Schema of implicit rating information collection section

18 SOFSEM06 17 Experimental XML document XML schema structure of faculty contents

19 SOFSEM06 18 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 SOFSEM06 19 Implicit Rating Information Module

21 SOFSEM06 20 CBR feature Information Collection Module Purpose –Collect CBR feature information to extract users 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 users implicit rating information.

22 SOFSEM06 21 CBR feature Information Collection Module Configuration of CBR feature collection technique Schema structure of CBR feature collection section

23 SOFSEM06 22 CBR feature Information Collection Module

24 SOFSEM06 23 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-N similarities.

25 SOFSEM06 24 Proposed Personalized Recommendation System Configuration of proposed system using CBR technique Personalized Rating Information Calculation Module Element weight Database

26 SOFSEM06 25 Personalized Rating Information Calculation Module Purpose –Compute users 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 contents 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 SOFSEM06 26 Personalized Rating Information Calculation Module Rating information of the content –V is the set of elements in the XML content the user accessed. –l e is the level weight of the element e. –k e is the element weight of e. –R c is the implicit rating information.

28 SOFSEM06 27 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 users 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 SOFSEM06 28 User Profile Management Module Select contents –Select contents whose implicit rating value(R c ) 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 SOFSEM06 29 User Profile Management Module Intra-attribute weights –The intra-attribute weights R of A i is {r i1, r i2, ···, r im }. k ij is the number of times a ij is accessed. r ij represents how much a user prefers the attribute value a ij to other attribute values. i = 1, 2, ···, n, and j = 1, 2, ···, m.

31 SOFSEM06 30 User Profile Management Module Intra-attribute weights User profile Userid (u) gdhong Attribute (A) Attribute Value (a i1..a im ) Appear Count (k ij ) Intra- attribute weight (R) Inter- attribute Weight (D) Major Database7- - Animation1- Network2- position Professor4- - Researcher3- Post-Doc3- Location Pusan2- - Seoul8- r ij ? Compute r ij of A 1 (Major) Attribute value Appear count Intra- attribute weight a 11 Databasek 11 7r 11 0.7 a 12 Animationk 12 1r 12 0.1 a 13 Networkk 13 2r 13 0.2

32 SOFSEM06 31 User Profile Management Module Inter-attribute weights –The inter-attribute weights D of A is {d 1, d 2, ···, d n }. each d i represents how much A i is preferred by the user. –If d i is large, the attribute A i is more important to the user than other attributes.

33 SOFSEM06 32 User Profile Management Module Inter-attribute weights d 1 of Major(A1) = 0.7 – (1/3) = 0.4 d 2 of Position(A2) = 0.4 – (1/3) = 0.1 d 3 of Location(A3) = 0.8 – (1/2) = 0.3 each d i of A i (Attribute) Attribute Inter-attribute Weight A1A1 Majord1d1 0.4 A2A2 Positiond2d2 0.1 A3A3 Locationd3d3 0.3 User profile Userid (u) gdhong Attribute (A) Attribute Value (a i1..a im ) Appear Count (k ij ) Intra- attribute weight (R) Inter- attribute Weight (D) Major Database7 0.7 - Animation1 0.1 Network2 0.2 Position Professor4 0.4 - Researcher3 0.3 Post-Doc3 0.3 Location Pusan2 0.2 - Seoul8 0.8 di ?di ?

34 SOFSEM06 33 Contents Recommendation Module –Analyze individual users 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 a ij is the attribute value of A i in P a ij is that of I if a ij = a ij, f (a ij, a ij ) returns 1 and otherwise, 0.

35 SOFSEM06 34 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 SOFSEM06 35 Experiment for Personal Information Collection System PRML instance

37 SOFSEM06 36 Experiment for Proposed Recommendation System User profile Userid (u) gdhong Attribute Of item (A) Attribute Value (a i1..a im ) Appear Count (k ij ) Intra-attribute weight (R) Inter-attribute Weight (D) Major Database70.7 0.4 Animation10.1 Network20.2 Position Professor40.4 0.1 Researcher30.3 Post-Doc30.3 Location Pusan20.2 0.3 Seoul80.8

38 SOFSEM06 37 Experiment for Proposed Recommendation System Experimental Results of recommendation –Use MAE(Mean Absolute Error) and ROC(Receiver Operating Characteristic)

39 SOFSEM06 38 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 contents feature. Research Log Ontology to represent users behavior patterns.


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