Transcript Document 7258174
SOFSEM’06
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
Changwon Nat
i Univ.
ISIE 2001
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.
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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.
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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
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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.
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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.
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Proposed System
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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.
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Personal Information Collection System
Existing method to collect user’s behavior 8
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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.
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Personal Information Collection System
Configuration of personal information collection system 10
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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 User Request/ Server Response 1
…
m
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Implicit rating Information 0
…
m CBR-Based Feature Information 0
…
m
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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.
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User Session Management Module
Schema structure of personal identification information section in PRML 13
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User Session Management Module
Example of personalized identification information section in PRML instance
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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.
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Implicit Rating Information Collection Module
Configuration of implicit rating collection technique
Schema of implicit rating information collection section 16
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Experimental XML document
XML schema structure of faculty contents 17
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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 18
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Implicit Rating Information Module
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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.
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CBR feature Information Collection Module
Configuration of CBR feature collection technique
Schema structure of CBR feature collection section 21
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CBR feature Information Collection Module
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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.
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Proposed Personalized Recommendation System
Configuration of proposed system using CBR technique 24
Personalized Rating Information Calculation Module Element weight Database
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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.
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Personalized Rating Information Calculation Module
Rating information of the content
R c
e
V l e
k e
– – – –
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.
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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.
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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.
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User Profile Management Module
Intra-attribute weights
– The intra-attribute weights
R
of
A i
is {
r i
1
, r i2 , ···, r im
}.
r ij
k ij
m p
1
k ip
,
i
= 1, 2,
···, n
, and
j
= 1, 2,
···
,
m
. • •
k ij
is the number of times
a ij
is accessed.
r ij
represents how much a user prefers the attribute value attribute values.
a ij
to other
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User Profile Management Module
Intra-attribute weights Userid (u) Attribute (A) Attribute Value (
a
i1
..a
im
)
User profile
gdhong Appear Count
(k
i j
)
Intra attribute weight (R) Inter attribute Weight (D) Database 7
-
Major Animation 1
-
Network 2
-
position Location Professor Researcher Post-Doc Pusan Seoul 4 3 3 2 8
-
r ij
?
Attribute value
a 11 a 12 a 13 Compute r ij of A 1
(Major)
Database Animation Network
Appear count
k 11 k 12 k 13
7 1 2
r
Intra attribute weight
11 r 12 0.7
0.1
r 13 0.2
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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.
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User Profile Management Module
Inter-attribute weights Userid (u) Attribute (A) User profile gdhong Attribute Value (
a
i1
..a
im
) Appear Count
(k
i j
)
Intra attribute weight (R) Inter attribute Weight (D) Database 7
0.7
-
Major Animation 1 Position Location Network Professor Researcher Post-Doc Pusan Seoul 2 4 3 3 2 8
0.1
0.2
0.4
0.3
0.3
0.2
0.8
-
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d i
?
d 1 d 2 d 3 of Major(A1) = 0.7 – (1/3) = 0.4
of Position(A2) = 0.4 – (1/3) = 0.1
of Location(A3) = 0.8 – (1/2) = 0.3
each d i of A i (Attribute)
A 1 A 2 A 3
Attribute Major Position Location Inter-attribute Weight
d 1 0.4
d 2 0.1
d 3 0.3
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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
•
a ij
is the attribute value of
A i
•
a’ ij
is that of • if
a ij = a’ ij
,
I f (a ij , a’ ij )
in returns
1 P
and otherwise,
0
.
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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.
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Experiment for Personal Information Collection System
PRML instance 35
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Experiment for Proposed Recommendation System
User profile Attribute Of item (A) Major Position Location Userid (u) Attribute ( Value
a
i1
..a
im
) Database Animation Network Professor Researcher Post-Doc Pusan Seoul User profile gdhong Appear Count
(k
i j
)
3 3 2 8 7 1 2 4 Intra-attribute weight (R)
0.7
0.1
0.2
0.4
0.3
0.3
0.2
0.8
Inter-attribute Weight (D)
0.4
0.1
0.3
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Experiment for Proposed Recommendation System
Experimental Results of recommendation
– Use MAE(Mean Absolute Error) and ROC(Receiver Operating Characteristic)
Existing Method vs. Proposed Method
1 0 3 2 Demographic CF Proposed MAE Sensitivity Specificity Accuracy Error rate
Rating Method 37
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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.
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