inferring contextual user proﬁles - improving recommender performance

OutlineContext-awareness

ApproachExperiments

ResultsConclusions

Inferring Contextual User Profiles -Improving Recommender Performance

Alan Said Ernesto W. De Luca Sahin Albayrak

{alan, deluca, sahin}@dai-lab.deDAI-LabTU-Berlin

CARS, Oct. 23, 2011

Said, De Luca, Albayrak Inferring Contextual User Profiles 1 / 17


ApproachExperiments

ResultsConclusions

Outline

Context-awareness

Approach

Experiments

Results

Conclusions



ApproachExperiments

ResultsConclusions

Abstract

Problem: The situation in which an event occurs has an effecton how we perceive the event, i.e. it changes our taste.For instance, the same movie seen under two differentcircumstances might get two different ratings by the same user.

Aim: The aim of this work is to improve recommendations byidentifying the situation in which a movie was seen.



ApproachExperiments

ResultsConclusions

Context-awareness

I Definition: ”Context is any information that can be used tocharacterize the situation of an entity” [Dey, 2001]

I Assumption: the behavior/taste of a user is dependent of thesituation.

I Goal: infer the situation from given data, recommend moviesbased on situation.



ApproachExperiments

ResultsConclusions

Identifying the situation

Using the information on when a movie rating occurred togetherwith the information on when a movie was shown in the cinema -an assumption on where the movie was seen is made.



ApproachExperiments

ResultsConclusions

Contextual User Profiles (CUPs)

Users behave differently when watching a movie at home comparedto watching it at the cinema - this is reflected in the way they ratemovies.Thus, each user has (at least) one home CUP , and one cinemaCUP.



ApproachExperiments

ResultsConclusions

Contextual User Profiles

Each user’s ratings are assigned to one out of two rating CUPs

ui uj uk um ul

ma 1 3 5

mb 4 4

mc 5 2

md 5 3 3

me 3 4 1 1



ApproachExperiments

ResultsConclusions

Contextual User Profiles

Each user’s ratings are assigned to one out of two rating CUPs

ui uj uk um ul

ma 1 3 5

mb 4 4

mc 5 2

md 5 3 3

me 3 4 1 1

um ulhome cinema home cinema home cinema cinema home

ma 1 3 5

mb 4 4

mc 5 2

md 5 3 3

me 3 4 1 1

ui uj uk



ApproachExperiments

ResultsConclusions

CUP neighborhoods

CUP-based neighborhoods are more fine grained than regular ones.

Regular neighborhood CUP neighborhood



ApproachExperiments

ResultsConclusions

Goal

Identify the situation of an event in order to:

I improve overall recommendation quality

I provide situation-specific recommendation



ApproachExperiments

ResultsConclusions

Dataset

I from moviepilot.de

I 1.5 million ratings

I 10, 000 usersI 7, 500 “cinema” Contextual User Profiles

I users with ratings within 2 months of premiere date

I 4, 700 “home” Contextual User ProfilesI users with ratings performed 2+ months after premiere



ApproachExperiments

ResultsConclusions

Setup

I kNN recommender using Pearson correlation, K = 150

I 50-fold random cross-validation

I true positives = movies rated above each user’s average rating

I compared to un-contextual baseline recommender using sameK and training/test splits



ApproachExperiments

ResultsConclusions

Precision

100% 100%

100%

100%

100% 206%

165% 146% 99%

87%

288%

187%

186% 138%

103% 204% 165% 145%

99% 86%

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0,018

1 5 10 50 100

Pre

cisi

on

N

Original Profiles

CUPs

CUPs Home

CUPs Cinema



ApproachExperiments

ResultsConclusions

Recall

100% 100% 100%

100%

100%

280% 204% 195%

143%

129%

1173%

666% 570%

387%

286%

259% 191% 183%

135%

124%

0,00E+00

5,00E-03

1,00E-02

1,50E-02

2,00E-02

2,50E-02

3,00E-02

1 5 10 50 100

Re

call

N

Original Profiles

CUPs

CUPs Home

CUPs Cinema



ApproachExperiments

ResultsConclusions

Mean Average Precision

Recommender MAP % improvement

Original users 5.26E − 3 0%

Contextual user profiles 6.05E − 3 15%

Home Context 7.97E − 3 51%

Cinema Context 6.00E − 3 14%



ApproachExperiments

ResultsConclusions

Conclusions

I ConclusionsI Inferring “simple” context is trivial – benefits are quite high.I Using this automated context-awareness can improve movie

recommendations.

I Future workI Explore less trivial contextI Collect feedback from usersI Use more elaborate techniques for inference



ApproachExperiments

ResultsConclusions

Thank you!

Questions?



ApproachExperiments

ResultsConclusions

CaRR2012

2nd Workshop on Context-awarenessin Retrieval and Recommendation inconjunction IUI 2012.

I Submission deadline: Dec. 2011

I When: February 14th, 2012

I Where: Lisbon, Portugal

I URL: www.carr-workshop.org

I Twitter: @CaRRws

Content and Goals of CaRR 2012Context-aware information is widely available in various ways and is be-coming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Further relevant issues are personalizing and adapting the information and the way it is displayed to the user’s cur-rent situation and interests. Ubiquitous computing furher provides new means for capturing user feedback on items and providing information.The aim of the 2nd Workshop on Context-awareness in Retrieval and Recommendation is to invite the community to discuss new creative ways to handle context-awareness. Furthermore, the workshop aims on exchanging new ideas between different communities involved in research, such as HCI, machine learning, information retrieval and rec-ommendation.

2nd Workshop on Context-awareness in Retrieval and Recommendationin Conjunction with IUI 2012, Lisbon, Portugal

Important Dates (tentative) n Submission: End of Dec 2012 n Notification: tbd n Camera Ready: tbd n Workshop: February 14, 2012

Further Information n Web: http://carr-workshop.org n E-Mail: [email protected] n Twitter: @CaRRws

Chairs n Ernesto de Luca, TU Berlin n Matthias Böhmer, DFKI n Alan Said, TU Berlin n Ed Chi, Google

Program Committe (tentative)Omar Alonso • Linas Baltrunas • Li Chen • Brijnesh-Johannes Jain •

Dietmar Jannach • Alexandros Karatzoglou • Carsten Kessler • Antonio Krüger • Michael Kruppa • Ulf Leser • Pasquale Lops • Till Plumbaum • Francesco Ricci • Markus Schedl (to be extended)


inferring contextual user proﬁles - improving recommender performance

Technology