Managing Irrelevant Contextual Categories in a Movie Recommender System

University of Ljubljana ..: Faculty of Electrical Engineering

[LDOS] ..: Digital Signal, Image and Video Processing Laboratory

Managing Irrelevant Contextual Categories in a Movie

Recommender System

Ante Odić, Marko Tkalčič, Andrej Košir



Introduction

Context aware recommender systems Users‘ decision are context dependent

Previous work methodology for detecting the relevancy of contextual factors

ODIĆ, Ante, TKALČIČ, Marko, TASIČ, Jurij F., KOŠIR, Andrej. Predicting and detecting the relevant contextual information in a movie-

recommender system. Interact. comput.. [Print ed.], 2013, vol. 25, no. 1, pp. 74-90, ilustr., doi:10.1093/iwc/iws003. [COBISS.SI-ID 9650260]

ODIĆ, Ante, TKALČIČ, Marko, TASIČ, Jurij F., KOŠIR, Andrej. Impact of the context relevancy on ratings prediction in a movie-

recommender system. Automatika (Zagreb), 2013, vol. 54, no. 2, pp. 252-262, ilustr., doi:10.7305/automatika.54-2.258. [COBISS.SI-

ID 9782356]

This work detection of relevant contextual conditions, i.e., the values of contextual factors, which

influence the users' decision making process

http://dx.doi.org/10.1093/iwc/iws003

http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9650260

http://dx.doi.org/10.7305/automatika.54-2.258

http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9782356



Problem statement - Sparsity

ratings are distributed into many categories

All categories

Only relevant categories

ratings

ratings

C1 C2 C3 C4

C1

C2

C3

C4



Problem statement - Questionnaire size

effort required from a user

All categories Only relevant categories



Solution

Proposed solutions

Sparsity

• manage irrelevant categories during training to utilize provided ratings

Questionnaire size

• identify contextual conditions which should be avoided or merged in

questionnaires



Method

Contextual factor

C1 C2 C3 C4 C5 … Cn

contextual-conditions-relevancy detection

Contextual factor

C1 C2 C3 C4 C5 … Cn

Yes No Yes Yes No … Yes

contextual-conditions-merges

determination

Contextual factor

C1 C2 C3 C4 C5 … Cn

C1 C2 + C1 C3 C4 C5 + C3 … Cn

improving the

questionnaireimproving the model



Dataset: LDOS-CoMoDa

Context Movie Dataset

Ratings provided emediately after the consumption

Context describing the consumption stage

users 89

items 946

ratings 1611

time day type season location weather social end emo. dom. emo. mood physical decision interaction

morning working day spring homesunny/

clearalone sad sad positive healthy user's choice first

afternoon weekend summer public place rainy partner happy happy neutral ill given by other n-th

evening holiday autumn friend's house stormy friends scared scared negative

night winter snowy colleagues surprised surprised

cloudy parents angry angry

public disgusted disgusted

family neutral neutral



Contextual-condition-relevancy detection

Wilcoxon rank-sum test

Comparing the distribution of ratings

• e.g., ratings when sunny weather vs ratings when any other weather condition (rainy, cloudy,

snowy or stormy)

Non-normal distribution

H0: two populations are the same

H1: perticular distribution tends to have larger values

If H1 => contextual condition is relevant



Contextual-Condition-Merges Determination

Wilcoxon rank-sum test

Comparing the distribution of ratings

• each relevant vs. each irrelevant

H0: two populations are the same

H1: perticular distribution tends to have larger values

If H0 => merge!



Merging in Questionnaires

Example: season

If possible, a new name for the combined condition could be used

Similar would be done during processing sensor data

spring

summer

autumn

winter

spring

winter

summer/autumn



Merging during modeling

Contextualized matrix factorization

Training

Without merging

With merging (summer autumn)

u

T

huh pqcbbchur )(),,(ˆ

)(autumnbu

),,( summerhur

),,( summerhurupdate

)(summerbu

),,( summerhurupdate

update )(summerbu

),,( autumnhurupdate

)(autumnbu

)(autumnbu),,( autumnhur

update



Results - merges

All categories irrelevant

No merges possible

time, daytype, location

All categories relevant

No merges needed

decision, interaction, physical



Results – rating prediction

We compare:

Merge vs. Basic (no merge)

Merge vs. random merge

Random merge

Same number of merges between random categories



Results – rating prediction



Discussion and future work

all contextual categories irrelevant => contextuar factor irrelevant

Issues

distributions might be similar, jet, for different user-item pairs ratings might be drastically different on

different contextual conditions

merge conflicts in questionnaire



LDOS-CoMoDa Dataset available at:

www.ldos.si/comoda.html

Thank you!

Managing Irrelevant Contextual Categories in a Movie Recommender System

Technology

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