Exploiting Contextual Information from Event Logs for Personalized Recommendation ICIS2010 Intelligent Database Systems Lab. School of Computer Science & Engineering Seoul National University Center for E-Business Technology Seoul National University Seoul, Korea Dongjoo Lee, Sung Eun Park, Minsuk Kahng, Sangkeun Lee, and Sang-goo Lee Aug 18,2010
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Exploiting Contextual Information from Event Logs for Personalized Recommendation
Exploiting Contextual Information from Event Logs for Personalized Recommendation. ICIS2010. Aug 18,2010. Dongjoo Lee, Sung Eun Park, Minsuk Kahng, Sangkeun Lee, and Sang-goo Lee. Intelligent Database Systems Lab. School of Computer Science & Engineering Seoul National University. - PowerPoint PPT Presentation
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Exploiting Contextual Information from Event Logs for Personalized Recommendation
ICIS2010
Intelligent Database Systems Lab.
School of Computer Science & Engineering
Seoul National University
Center for E-Business TechnologySeoul National UniversitySeoul, Korea
Dongjoo Lee, Sung Eun Park, Minsuk Kahng, Sangkeun Lee, and Sang-goo Lee
Aug 18,2010
Copyright 2010 by CEBT
Contents
Introduction
Recommendation and Personalized Recommendation
Recommendation Space
Personalized Recommendation
Exploiting Contextual Information for Recommendation
Context and Context Abstraction
Context-Aware Recommendation Algorithm
Experiment
Conclusion
2
Copyright 2010 by CEBT
Contents
Introduction
Personalized Recommendation
Exploiting Contextual Information for Recommendation
Experiment
Conclusion
3
Copyright 2010 by CEBT
Motivation
Do you always get same list no matter what the context is?
We recommend items depending on the context4
08/05/29 04:4308/05/29 04:43
08/08/02 08:5108/08/02 08:51
08/10/28 09:5908/10/28 09:59Song1Song2Song3
…
Song1Song2Song3
…
Song1Song2Song3
…
Song1Song2Song3
…
Song1Song2Song3
…
Song1Song2Song3
…
User User
List 1
List 1
List 108/05/29 04:4308/05/29 04:43
08/08/02 08:5108/08/02 08:51
08/10/28 09:5908/10/28 09:59Song1Song2Song3
…
Song1Song2Song3
…
Song3Song4Song5
…
Song3Song4Song5
…
Song4Song5Song6
…
Song4Song5Song6
…
User User
List 1
List 2
List 3
Copyright 2010 by CEBT
Assuming that there are enough number of ratings from users is less practical
Find implied patterns in preferences from logs and use it for the recommendation
Recommendation from Event Logs
5
Large Number of Users’ Log Data
Large Number of Users’ Log Data
user_id item timestamp
1432White winter hymnal
- Fleet foxes07/19/2008
16:55
1941Let's get out of this
country – Camera obscura
08/03/2008 22:14
1941White winter hymnal
- Fleet foxes08/10/2008
22:12
Listen
User_1432
07/19/2008 16:55
Listen
08/03/2008 22:14
User_1941
Implied Patterns in PreferencesImplied Patterns in Preferences
Use for the RecommendationUse for the Recommendation
Event Logs
Listen
08/10/2008 22:12
White winter hymnal
Let's get out of this country
Copyright 2010 by CEBT
Exploiting Information from Event Logs
We can obtain popularity, personal preference, and contextual information from log data, and consider them for recommendation
6
Event Logs(from large
accumulateddata)
Event Logs(from large
accumulateddata)
ContextualPreferences
PersonalPreferences
RecommenderSystem
RecommenderSystem
Personalization
Context-Awareness
1..
2.
AbstractingContextualInformation
AbstractingContextualInformation
Cooperating Achieved
Information
Cooperating Achieved
Information
Copyright 2010 by CEBT
Contents
Introduction
Recommendation
Recommendation Space
Collaborative Filtering
Exploiting Contextual Information for Recommendation
Experiment
Conclusion
7
Copyright 2010 by CEBT
Recommendation Space
Log
nl Event-logs
Each event-log is a tuple that consists of user, item, timestamp, GPS code and other context-related data.
Event-logs' (user, item, season, time of day, day of week, location, city, temperature, …)
Context AbstractionContext Abstraction
Copyright 2010 by CEBT
Context Abstraction
It can be subjective to map raw context value to exactly one concept
timestamp: 2009-09-03 00:00 -> summer? autumn?
temperature: 24℃ ->warm? hot?
Mapping based on Fuzzy Membership Function
Membership degree indicates how strongly an element belongs to the set
13
day0 365
winter winterspring summer autumn
Copyright 2010 by CEBT
Contents
Introduction
Recommendation
Exploiting Contextual Information for Personalized Recommendation
Context in Event Logs
Context Abstraction
Context-Aware Recommendation Algorithms
Experiment
Conclusion
14
Combining Collaborative Filtering and
Context-Aware Recommendation Method
Copyright 2010 by CEBT
Popularity-based Approach
Context-dependent popularity
pi,ctx : popularity of an item i in the context ctx
Integrate the contextual preference to Individual Preference
Simple weighted sum
15
General preference of item i in the context ctx
a’s Preference on item i
Copyright 2010 by CEBT
Personalized Context-Aware Recommendation
The rating of user uj for a song si in the context ctx
=The number of times that uj listened si in the ctx
Context-aware rating of users
16
item1 item2 … itemi
u1 5 3 3
u2 4 2 2
…
uj 5 5 4
item1
item2
… itemi
u1 Context1 5 1 0
Context2 0 2 2
…
Contextk 0 0 1
u2 Context1 4 0 2
Context2 0 1 0
…
Contextk 0 1 0
…
uj Context1 0 1 2
Context2 0 1 0
…
Contextk 5 3 2
Copyright 2010 by CEBT
Personalized Context-Aware Recommendation
Two possible scenarios to find similar users
17
Similarity in Summer : 0.4
Overall Similarity : 0.5
Overall Similarity : 0.7
Similarity in Summer : 0.6
Song1Song2Song3
…
Song1Song2Song3
…
Song4Song5Song3
…
Song4Song5Song3
…
Items of Summer
Items of Summer
In Summer, what will A like?
Copyright 2010 by CEBT
item1 item2 … itemi
u1 Context1
5 0 0
Context2
0 3 2
…
Contextk
0 0 1
u2 Context1
4 0 2
Context2
0 1 0
…
Contextk
0 1 0
…
uj Context1
0 1 2
Context2
0 1 0
…
Contextk
5 3 2
Personalized Context-Aware Recommendation
Two approaches to find similar users
1) find users with similar ratings regardless of context type, and recommend items to the current context based on the similar context previously preferred by the same user
18
item1 item2 … itemi
u1 5 3 3
u2 4 2 2
…
uj 5 5 4
Data to Consider for determining similarity of
usersData to Consider for
recognizing the rating
Copyright 2010 by CEBT
Personalized Context-Aware Recommendation
Two approaches
2) find users with similar ratings with respect to the context type, and recommend items to the current context based on the similar context previously preferred by the same user
19
item1 item2 … itemi
u1 5 3 3
u2 4 2 2
…
uj 5 5 4
Data to Consider for determining similarity of
usersData to Consider for
recognizing the rating
item1 item2 … itemi
u1 Context1
5 0 0
Context2
0 3 2
…
Contextk
0 0 1
u2 Context1
4 0 2
Context2
0 1 0
…
Contextk
0 1 0
…
uj Context1
0 1 2
Context2
0 1 0
…
Contextk
5 3 2
Copyright 2010 by CEBT
Reduction-based Context Aware method
– Conjunction of context concepts ( ex. Winter& Monday &Night)
Adomavicius et al., Incorporating contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems 2005
– Data sparsity problem
Disjunction-based Context Aware method applies collaborative filtering to data in each context concept and aggregate the result disjunctively