addressing the new user problem with a personality based user similarity measure

Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..

[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..

Addressing the New User Problem with a

Personality Based User Similarity Measure

Marko Tkalčič, Matevž Kunaver, Andrej Košir and Jurij Tasič



Overview

Area overview

Problem statement

Proposed solution

Methodology

Results

Conclusion



Collaborative filtering recommender systems

Which film should I watch tonight?




Users space





User similarity measure


Users space



User Similarity Measure (USM)

Baseline: rating-based USM

– Based on overlapping ratings

– Estimation of rating for observed user

Few ratings (m)NEW USER PROBLEM

(NUP) COLD START PROBLEM

(CSP)



Problem statement

Number of overlapping ratings m

RS performance



Problem statement

1. How to minimize the CSP?


RS performance



Problem statement

1. How to minimize the NUP?

2. Where is the CSP boundary?


RS performance

?



Proposed solution

1. How to minimize the CSP?

– Personality based USM

– Why? users with similar personalities tend to prefer similar items (presumption)

– + No need for ratings

– - Need for a startup questionnaire to assess personality

2. Where is the CSP boundary?

– Statistical test:

• H0: avg(Fs) = avg(Fs+1 )



What is personality?

... personality refers to the enduring patterns of thought, feeling, motivation and

behaviour that are expressed in different circumstances [Westen1999]

Five Factor Model (FFM)

FFM is a hierarchical organization of personality traits in terms of five basic

dimensions:

– extraversion (E),

– agreeableness (A),

– conscientiousness (C),

– neuroticism (N)

– openness (O)

These are the most important ways in which the individuals differ in their enduring

emotional, interpersonal, experiential, attitudinal and motivational styles



Methodology

Recommender system‘s task: estimate unrated items

Offline

Compare performance

– Baseline USM at different stages s (= CSP simulation)

– Personality based USM

Each user u is modeled with a five tuple b=(b1, b2, b3, b4, b5)

FFM acquisition with IPIP questionnaire (50 questions)

Distance between 2 users ui and uj

7 neighbors

Rating estimation

Evaluation:

– confusion matrix for ratings estimations F measure

– T test



Dataset

LDOS-PerAff-1:

– I = 52 users

– J = 70 items (images from IAPS dataset)



Results 1: p-values of USM comparison



Results 2: p- values for CSP boundary



Conclusions

Proposed USM performs better in under cold start circumstances

– i.e. Personality accounts for between-users variance in entertainment apps

Proposed USM is equivalent under normal (non-CS) conditions

Drawbacks:

– Need for FFM assessment

– Ethical/privacy issues

– Evaluated on this dataset only



Thank you



IPIP questionnaire

Freely available



Results: F measure boxplots

addressing the new user problem with a personality based user similarity measure

Technology

videawhich film should

where is the csp boundary

videaproblem statementhow

videaproposed solutionhow

minimize the csp