HIDDEN CONCEPT DETECTION IN GRAPH-BASED RANKING ALGORITHM FOR PERSONALIZED RECOMMENDATIONNan Li

Computer Science Department

Carnegie Mellon University

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.

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.

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.

PRA MODEL

(G, l, θ)• The feature matrix A has its each column to be

the distribution hp(e).

• The scoring function:

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:

HIDDEN CONCEPT DETECTOR (HCD)

Two-Layer PRA

paper

author

gene

title journal

year

paper

author

gene

title journal

year

Find hidden subtype of relations

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

APPROXIMATE THE GAIN OF OBJECTIVE FUNCTION

1. Calculate the maximum gain of two relations: gm and gn

2. Use taylor series to approximate:

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.

NORMALIZED CUT

Training data: Number of clusters ↑

Recommendation quality↑

Test data: NCut outperforms random

HCD

• Training data:• HCD outperforms PRA

in all three measurements

• Test data:• Two systems perform

equally well

FUTURE WORK

Bottom-Up vs Top Down Improve Efficiency Type Recovery in Non-Labeled Graph

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

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)

A COMPUTATIONAL MODEL

Model Accelerated Future Learning Use Machine Learning Techniques Acquire Deep Feature Integrated into a Machine-Learning Agent

AN EXAMPLE IN ALGEBRA

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

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

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

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)

GREEDY STRUCTURE HYPOTHESIZER

Structure learning Bottom-up Prefer recursive to non-recursive

EM PHASE

Step One: Plan parse tree

computation Most probable parse

tree Step Two:

Selection probabilities update

s: ai p, aj ak

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

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

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

EXPERIMENT DESIGN IN ALGEBRA

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

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

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.

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

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.