Learning user preferences for 2CP-regression for a recommender system Alan Eckhardt, Peter Vojtáš Department of Software Engineering, Charles University

Learning user preferences for 2CP-regression for a

recommender system

Alan Eckhardt, Peter Vojtáš

Department of Software Engineering,

Charles University in Prague,

Czech Republic

SOFSEM 2010, Špindlerův mlýv, Czech Republic, 23.-29.1.2010

Outline

Motivation User model Peak and 2CP Experiments Conclusion and future work


User preference learning

Helping the user to find what she looks for E.g. notebooks

A small amount of information required from the user Ratings of notebooks,...

Construction of a general user preference model Each user has his/her own preference model

Recommendation of the top k notebooks to the user Which the preference model has chosen as the most

preferred for the user


User preference learning

Recommendation process Initial set

Centers of clusters of objects

Construction of user model

Recommendation

More iterations possible In each iteration the user

model is refined

Initial set

Recommendersystem

User

Construction of user model

User decision making

Recommended items


Two step user model

User model learning is divided into two steps1. Local preferences - normalization of the

attribute values of notebooks to their preference degrees

Transforms the space into [0,1]N

2. Global preferences - aggregation of preference degrees of attribute values into the predicted rating

1,0: iAi Df

1,01,0:@ N

iAD


User model

Fuzzy sets Normalize the space to monotone space [0,1]N

Define pareto front Set of incomparable objects Candidates for the best object

(1,…,1) is the best object1

500

0

0

f Price

2 000

Price [$]

100

1

10


User model

Aggregation Resolves the best object from pareto front The second best object may not be on

pareto front Two methods – Statistical and Instances

9

Price_U*3CPU_U *1_UR*5

Price_U ,CPU_U ,RAM_U@

111

111

AM

1

10

1st best

2nd best


Normalization of numerical attributes

Linear regression Preference of the smallest or

the largest value

Quadratic regression Can detect ideal values, but

often fails in experiments

0

0

1

10 000 [$]

Rating

Price

0

0

1

10 000 [$]

Rating

Price


2CP regression

Preference dependence between attributes This is not a dependence

in the dataset (e.g. the resolution of display influences the price)

The influence of the value of attribute A1 on the preference of attribute A2

Manufacturer

Price

Name

ACER

ASUS

FUJITSU

MSI

Name

TOSHIBA

HP

IBM

SONY

LENOVO

ACER, ASUS,

FUJITSU, MSI

TOSHIBA, HP, IBM,

SONY, LENOVO 2200$

750$

Rating

0.2

0.5

0.8

0.5

Rating

0.7

0.9

0.8

0.7

0.4

Manufacturer Ideal Price

E.g. the value of the producer (IBM) of a notebook influence the preference of the price of the notebook (for IBM, the ideal price is 2200$).


Peak

Motivation User often prefer once particular

value of attribute

Finding the peak value Traversing the training set

Which is small Testing the error of linear regressions

on both sides of the peak

We know exactly which value is the most preferred Useful for visual representation

0

0

1

10 000 [$]

PeakRating

Price


2CP regression+Peak

Dependence of price on the value of manufacturer ACER => High price ASUS => Lower price

Manufacturer

Price

Name

ASUS

Name

ACER


Experiment settings

Dataset of 200 notebooks Artificial user preferences

The preference of price was dependent on the value of producer

Training sets of sizes 2-60 The rest of the dataset was

used as testing set

Error measures RMSE Kendall coefficient


Experiment settings

Tested methods Support Vector Machines from Weka Mean – returns the average rating from the training set Instances – classification, uses objects from the training as

boundaries on rating Statistical – weighted average with learned weights

2CP Both Instances and Statistical can use local preference

normalization – Linear, Quadratic, Peak 2CP serves to find the relation between the preference of an

attribute value and the value of another


Experiment results


Experiment results


Experiment results


Conclusion

Proposal of method Peak Combination with 2CP Experimental evaluation with very good

results Using rank correlation measure


Future work

nCP-regression Clustering of similar values for better

robustness Degree of relation between two attributes

Documents

Learning user preferences for 2CP-regression for a recommender system Alan Eckhardt, Peter Vojtáš Department of Software Engineering, Charles University