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Hideaki Takeda / National Institute of Informatics NII
Intelligent information integrationPart II: Information Gathering
Hideaki Takeda
National Institute of Informatics
takeda@nii.ac.jp
http://www-kasm.nii.ac.jp/~takeda/
Hideaki Takeda / National Institute of Informatics NII
Intelligent Information Integration
Information Gathering
Information Integration
Information Distribution
Hideaki Takeda / National Institute of Informatics NII
Information Gathering
Model: Relationship between agents and information sources
Main task: how to provide access from the agent to information sources
IS
IS
IS
Hideaki Takeda / National Institute of Informatics NII
Methods for Information Gathering
Information Retrieval
Explicit specification of users needs
Currently available information sources
Information Filtering
Dynamic information sources
Not available at the current moment, but available in future
Browsing
Implicit specification of users needs
Hideaki Takeda / National Institute of Informatics NII
Browsing
Characteristics as Information Gathering
Pros:
Users initiative
Applicable even with vague purpose
Cons: No warranty to reach the goal
Human habit: up to down, side trip
Human limitation: Sequential access
Problems
How to support users with keeping users initiative
How to obtain users preference
Hideaki Takeda / National Institute of Informatics NII
Browsing
Problems
How to support users with keeping users initiative
How to obtain users preference
How to obtain users preference
Web Watcher
Letizia
Syskill & Webert
InforSpinder
Hideaki Takeda / National Institute of Informatics NII
Web Watcher
Hideaki Takeda / National Institute of Informatics NII
Web Watcher
Use of machine learning
Learn users preference from browsing process
Functions
Recommendation of links which the system infers useful to the user among links in browsing pages
Recommendation of links which the systems infers useful to the user among all links
Hideaki Takeda / National Institute of Informatics NII
Web Watcher
Learning target
LinkUtility: Page×Goal×User×Link→[0,1]
UserChoice: Page×Goal×Link→[0,1]
Page: Keyword vector of 200 words extracted from pages
link: Keyword vector of 200 words from links and 100 words from texts surrounding links
Goal: Keyword vector of 30 words
Hideaki Takeda / National Institute of Informatics NII
Web Watcher
Learning Method
TFIDF(term frequency times inverse document frequency) [Salton] and other two methods
Results
2 or 3 times better than random recommendation
Hideaki Takeda / National Institute of Informatics NII
TF/IDF
To measure importance of document d within document set D by word t
W(d,t,D) = TF(d,t)・IDF(t,D)
TF(d,t): frequency of word t in document d
IDF(t,D) = log(|D|/freq(D,t))
freq(D, t): number of document in D in which t appears
Intuitively
More frequent the specific word appears, more important
But, more documents have the specific words, less important
Hideaki Takeda / National Institute of Informatics NII
Use of TF/IDF in WebWatcher
TF/IDF is calculated for the goal page
Then each vector for “userchoice” is calculated with TF/IDF values for words as weight
Welcome from the dean
0.0 Baseball0.7 welcome0.0 graphics0.1 the0.0 hockey…0.2 from0.0 space0.7 dean
TFIDF weights
=1.7
Hideaki Takeda / National Institute of Informatics NII
Reinforcement Learning
A model that an agent can learn its behavior through trial-and-error in a dynamic environment
Learning Agent
Environment
Action state Reward
Hideaki Takeda / National Institute of Informatics NII
Basic elements in reinforcement learning
Environment
The environment is (partially) observable by sensors
A set of states described by some parameters
Reinforcement (Reward) function
Reinforcement or reward is feedback from the environment to the agent
The goal of reinforcement learning is to find a function to maximize rewards that it will receive in the future
Examples for reward: delayed rewards, minimal time …
Hideaki Takeda / National Institute of Informatics NII
value function
policy
Decide next action in each state
Mapping from state to action
Optimal policy: mapping from any state to the state where maximal reward is given
state value
Sum of rewards from the initial state to the state
Value function
Mapping from a state to a state value
It depends on policy
Hideaki Takeda / National Institute of Informatics NII
The given environment
Gain
10
-1 -1
-1
-1
-1 -1
-1
-1-1-1
-1
Hideaki Takeda / National Institute of Informatics NII
Optimal state value
Gain
10
5
7
6
898
5
45
6
7
-1 -1
-1
-1
-1 -1
-1
-1-1-1
-1
Hideaki Takeda / National Institute of Informatics NII
Q-Learning
R(s): reward at state s
Q(s, a): evaluation function for action a at state s
Delayed rewards
)]','([)'(),(
)(),(
'1
10
asQsRasQ
SRaSQ
nactiona
n
iti
it
∈+
++
∞
=
+=
=∑maxλ
γ
1 2 3 4 Tγ γ γ γ
Hideaki Takeda / National Institute of Informatics NII
Q-learning in WebWatcher
Page: state
Hyperlink: action
Reward for page: how much it is interesting for user
It is measured by sum of words weighted by TFIDF
Hideaki Takeda / National Institute of Informatics NII
Letizia
Browser which learns in browsing
Functions
Recommend pages starting from the page which the user is currently browsing
Methods
Collect pages starting from the current page in breadth-first way
Stop by thresholds or if the current page is changed
TFIDF
Users profile
Hideaki Takeda / National Institute of Informatics NII
Letizia
Hideaki Takeda / National Institute of Informatics NII
Syskill & Webert
Browser which learn by Supervised Learning
User specify hot, cold, or lukewarm
System recommends either hot, cold, worth to look, or worth not to look
Hideaki Takeda / National Institute of Informatics NII
Selection of keywords
Calculation for keyword selection
where
( ) ( ) ( ) ( ) ( ) ( )[ ]E W S I S P W present I S P W absent I Sw present w absent, = − = + == =
( ) ( ) ( )( )I S p S p Sc c= −∑ log2
Hideaki Takeda / National Institute of Informatics NII
Selection of keywords
hot:30 cold:70
pr:20h&p:10 c&p:10
h&a:20 h&c:60ab:80
total:100
I I I I
I
I I I I
I
I I I I
I
E
h c
h p c p
p
h a c a
p
= = = === = = =
=
= = = =
=
= − ⋅ + ⋅ =
0 3 0 7
0 5 0 5
0 25 0 75
052 0 36
088
05 05
1
05 0 31
081
088 0 2 1 08 081 0 03
. .
| . | .
| . | .
. .
.
. .
. .
.
. ( . . . ) .
hot:30 cold:70
pr:20h&p:15 c&p:5
h&a:20 h&c:60ab:80
total:100
I I I I
I
I I I I
I
I I I I
I
E
h c
h p c p
p
h a c a
p
= = = === = = =
=
= = = =
=
= − ⋅ + ⋅ =
0 3 0 7
0 75 0 25
0 25 0 75
0 52 0 36
088
0 31 0 5
081
0 5 0 31
081
0 88 0 2 081 0 8 0 81 0 07
. .
| . | .
| . | .
. .
.
. .
.
. .
.
. ( . . . . ) .
Hideaki Takeda / National Institute of Informatics NII
InfoSpider[Menczer]
Adaptive information agent by Artificial Life
Agent follows links, then obtain energy dependent on page content and sometimes propagates
Identify pages related to the questions
Hideaki Takeda / National Institute of Informatics NII
InfoSpinder
Hideaki Takeda / National Institute of Informatics NII
Information Filtering / Recommendation system
Content-based filtering
Estimate users preference by comparing keywords in pages and users profiles
Social filtering / Collaborative filtering
Estimate users preference by collecting and analyzing preferences of many users
Hideaki Takeda / National Institute of Informatics NII
Problem definition
How to estimate missing information from the given matrix?
Each vector represents preference of each person
Some values are missing because she has not experience them
Estimate these values
Solution: Use similarity between users
Article Person A
1 1 4 2 22 5 2 4 43 34 2 5 55 4 1 16 ? 2 5
Person B Person C Person D
Hideaki Takeda / National Institute of Informatics NII
Algorithm for Social filtering(correlation coefficient)
11144
12)1(2
8.044111144
)2(12)1()1(212
=++⋅+−⋅−
=
−=++++++
−⋅+⋅−+−⋅+⋅−=
AC
AB
r
r
∑∑
∑
==
=
−−
−−=
===
'
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)()(
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n
lklk
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lkkl
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lklkkkl
kk
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xxxx
xxxxr
mkmkkkCov
r KKσσ
Article Person A
1 1 4 2 22 5 2 4 43 34 2 5 55 4 1 16 ? 2 5
Person B Person C Person D
∑
∑
=
=
−−=
−=
'
1''
'
1
2
))(()',(
)(
n
lklkkkl
n
lkklk
xxxxkkCov
xxσStandard deviation
Calculate relation between user k and k’ by correlation coefficient (相関係数)
Covariance
Hideaki Takeda / National Institute of Informatics NII
Algorithm for Cooperative filtering(correlation coefficient)
∑∑
≠
≠
−+=
kkkk
kkkkklk
kkl r
rxxxx
''
'''' )(
'
56.418.0
12)8.0()1(3' 6 =
+⋅+−⋅−
+=Ax
1 1 4 2 22 5 2 4 43 34 2 5 55 4 1 16 ? 2 5
Article Person A Person B Person C Person D
Hideaki Takeda / National Institute of Informatics NII
GroupLensCollaborative filtering system for NetNews
Hideaki Takeda / National Institute of Informatics NII
Collaborative Filtering: Pros and Cons
Pros
Robust for content change
No need for content analysis
Applicable for non-text data
Few users actions
Just only evaluate items
Cons
“Cold start” problem
Massive evaluation data is need before reliable recommendation
No evaluation, no recommendation
Items without evaluation are never recommended
Hideaki Takeda / National Institute of Informatics NII
MovieLens
Hideaki Takeda / National Institute of Informatics NII
Content-based filtering: pros and cons
Pros
Precise recommendation is possible
For users
For providers
Cons
For providers: Difficulty to design profiles
For users: Difficulty for keeping users profiles
Input
Update
Not adaptive for new contents
Hideaki Takeda / National Institute of Informatics NII
Lifestyle Finder
Recommendation for buying, visiting, etc
Estimate users preference by categorizing users to some clusters
Fewer feedback are needed for learning preference
Categorization of users with large-scale demographic data
Demographic data tell us a good example of users categorization
Ex., A survey categorizes Americans into 62 clusters by using U.S. census, magazine subscriptions, catalog purchases, …
Create larger clusters by merging the given clusters
Ex., “Prefer TV news” ⊃ “Prefer the specific TV news program”
Hideaki Takeda / National Institute of Informatics NII
Lifestyle Finder
Hideaki Takeda / National Institute of Informatics NII
Lifestyle Finder
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