H IDDEN C ONCEPT D ETECTION IN G RAPH - B ASED R ANKING A LGORITHM FOR P ERSONALIZED R ECOMMENDATION...
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Transcript of H IDDEN C ONCEPT D ETECTION IN G RAPH - B ASED R ANKING A LGORITHM FOR P ERSONALIZED R ECOMMENDATION...
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HIDDEN CONCEPT DETECTION IN GRAPH-BASED RANKING ALGORITHM FOR PERSONALIZED RECOMMENDATIONNan Li
Computer Science Department
Carnegie Mellon University
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INTRODUCTION
Previous work: Represents past user behavior through a
relational graph.
Fail to represent individual differences among items of a same type.
Our work: Detect hidden concepts embedded in the
original graph Build a two-level type hierarchy for explicit
representation of item characteristics.
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RELATIONAL RETRIEVAL
1. Entity-Relation Graph G=(E, T, R): • Entity set E={e} Entity types set T={T} Entity
relations R={R}• Each entity e in E has its type e.T .• Each relation R has two entity types R.T1 and
R.T2. If two entities has relation R, then R(e1, e2) = 1, o/w 0.
2. Relational Retrieval Task: Query q = (Eq , Tq)
• Given Eq = {e’}, predict the relevance of each entity e of the target type Tq.
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PATH RANKING ALGORITHM
1. Relational Path: P = (R1, R2, …, Rn) R1.T1=T0 and
Ri.T2=Ri+1.T1.
2. Relational Path Probability Distribution: The probability corresponds to the
probability of a path random walker reaching that entity from a query entity.
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PRA MODEL
(G, l, θ)• The feature matrix A has its each column to be
the distribution hp(e).
• The scoring function:
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TRAINING PRA MODEL
1. Training data: D = {(q(m),y(m))}, ye(m)=1 if e is
relevant to the query q(m)
2. Parameter: The weight of path θ
3. Objective function:
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HIDDEN CONCEPT DETECTOR (HCD)
Two-Layer PRA
paper
author
gene
title journal
year
paper
author
gene
title journal
year
Find hidden subtype of relations
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BOTTOM-UP HCD
Bottom-Up merging algorithm: For each relation type Ri
Step1: Divide every starting node of relation Ri as a subrelation Rij.
Step2: HAC: Each time merge two subrelations Rim and Rin to maximize the gain of objective functions until no positive gain:
paperauthor paperauthor
paperauthor paperauthor
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APPROXIMATE THE GAIN OF OBJECTIVE FUNCTION
1. Calculate the maximum gain of two relations: gm and gn
2. Use taylor series to approximate:
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EXPERIMENTAL RESULTS
1. Data Sets: Saccharomyces Genome Database, a publication data
set about the yeast organism Saccharomyces cerevisiae
2. Three measurements: Mean Reciprocal Rank (MRR): inverse of the rank of the
first correct answer Mean Average Precision (MAP): the area under the
Precision-Recall curve p@K: precision at K, where K is the actually number of
relevant entities.
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NORMALIZED CUT
Training data: Number of clusters ↑
Recommendation quality↑
Test data: NCut outperforms random
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HCD
• Training data:• HCD outperforms PRA
in all three measurements
• Test data:• Two systems perform
equally well
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FUTURE WORK
Bottom-Up vs Top Down Improve Efficiency Type Recovery in Non-Labeled Graph
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A COMPUTATIONAL MODEL OF ACCELERATED FUTURE LEARNING THROUGH FEATURE RECOGNITIONNan Li
Computer Science Department
Carnegie Mellon University
Building an intelligent agent that simulates human-level learning using machine learning techniques
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ACCELERATED FUTURE LEARNING
Accelerated Future Learning Learning more effectively because of prior
learning Has been observed a lot How?
Expert vs Novice Expert Deep functional feature (e.g. -3x -3) Novice Shallow perceptual feature (e.g. -3x 3)
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A COMPUTATIONAL MODEL
Model Accelerated Future Learning Use Machine Learning Techniques Acquire Deep Feature Integrated into a Machine-Learning Agent
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AN EXAMPLE IN ALGEBRA
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FEATURE RECOGNITION ASPCFG INDUCTION
Under lying structure in the problem Grammar
Feature Intermediate symbol in a grammar rule
Feature learning task Grammar induction Error Incorrect parsing
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PROBLEM STATEMENT
Input is a set of feature recognition records consisting of An original problem (e.g. -3x) The feature to be recognized (e.g. -3 in -3x)
Output A PCFG An intermediate symbol in a grammar rule
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ACCELERATED FUTURE LEARNING THROUGH FEATURE RECOGNITION
Extended a PCFG Learning Algorithm (Li et al., 2009)
Feature Learning Stronger Prior Knowledge:
Transfer Learning Using Prior Knowledge Better Learning Strategy:
Effective Learning Using Bracketing Constraint
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A TWO-STEP ALGORITHM
• Greedy Structure Hypothesizer: Hypothesizes the
schema structure
• Viterbi Training Phase: Refines schema
probabilities Removes redundant
schemas
Generalizes Inside-Outside Algorithm (Lary & Young, 1990)
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GREEDY STRUCTURE HYPOTHESIZER
Structure learning Bottom-up Prefer recursive to non-recursive
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EM PHASE
Step One: Plan parse tree
computation Most probable parse
tree Step Two:
Selection probabilities update
s: ai p, aj ak
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FEATURE LEARNING
Build Most Probable Parse Trees For all observation
sequences Select an
Intermediate Symbol that Matches the most
training records as the target feature
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TRANSFER LEARNING USING PRIOR KNOWLEDGE
GSH Phase: Build parse trees
based on previously acquired grammar
Then call the original GSH
Viterbi Training: Add rule frequency
in previous task to the current task
0.66
0.330.50.5
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EFFECTIVE LEARNING USING BRACKETING CONSTRAINT
Force to generate a feature symbol Learn a subgrammar
for feature Learn a grammar for
whole trace Combine two
grammars
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EXPERIMENT DESIGN IN ALGEBRA
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EXPERIMENT RESULT IN ALGEBRA
Fig.2. Curriculum one Fig.3. Curriculum two Fig.4. Curriculum three
Both stronger prior knowledge and a better learning strategy can yield accelerated future learning
Strong prior knowledge produces faster learning outcomes L00 generated human-like errors
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LEARNING SPEED INSYNTHETIC DOMAINS
Both stronger prior knowledge and a better learning strategy yield faster learning
Strong prior knowledge produces faster learning outcomes with small amount of training data, but not with large amount of data
Learning with subtask transfer shows larger difference, 1) training process; 2) low level symbols
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SCORE WITH INCREASING DOMAIN SIZES
The base learner, L00, shows the fastest drop Average time spent per training record
Less than 1 millisecond except for L10 (266 milliseconds) L10: Need to maintain previous knowledge, does not separate trace
into small traces Conciseness: Transfer learning doubled the size of the schema.
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INTEGRATING ACCELERATED FUTURE LEARNING IN SIMSTUDENT
Tutor LuckyNext Problem Quiz Lucky
Prepare Lucky for Quiz Level 3 !
Curriculum Browser
Level 1:[+] One-Step Linear Equation
Level 2:[+] Two-Step Linear Equation
Level 3:[-] Equation with Similar Terms
OverviewIn this unit, you will solve equations with integer or decimal coefficients, as well as equations involving more than one variable.
More…
Lucky
x+5
• A machine-learning agent that
• Acquires production rules from
• Examples and problem solving experience
• Integrate the acquired grammar into production rules Requires weak
operators (non-domain specific knowledge)
Less number of operators
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CONCLUDING REMARKS
Presented a computational model of human learning that yields accelerated future learning.
Showed Both stronger prior knowledge and a better
learning strategy improve learning efficiency. Stronger prior knowledge produced faster
learning outcomes than a better learning strategy.
Some model generated human-like errors, while others did no make any mistake.
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