Addressing the New User Problem with a Personality Based User Similarity Measure

Addressing the New User Problem with a
Personality Based User Similarity Measure

Marko Tkalčič, Matevž Kunaver, Andrej Košir and Jurij Tasič

Univerza v Ljubljani ..: Fakulteta za elektrotehniko:..
[LDOS] ..: Laboratorij za digitalno obdelavo signalov, slik in videa:..


Overview
 Area overview
 Problem statement
 Proposed solution
 Methodology
 Results
 Conclusion


Collaborative filtering recommender systems

Which film should
I watch tonight?



Which film should
I watch tonight?

Users space



Which film should User similarity measure
I watch tonight?

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

RS performance

Number of overlapping ratings m


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

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