ExpLOD: a framework for explaining recommendations based on the Linked Open Data Cloud

ExpLOD:
a Framework for Explaining
Recommendations
based on the
Linked Open Data Cloud
Cataldo Musto, Fedelucio Narducci, Pasquale Lops,
Marco de Gemmis, Giovanni Semeraro
Sept 15-19, 2016 - Boston
BPN

PROBLEM DEFINTION
➤ The recommendation process
is sometimes viewed from the
user side as an obscure
process
➤ The recommender usually
implements a black box
model with no knowledge
on its internal mechanisms
ExpLOD: a Framework for Explaining Recommendations based on the Linked Open Data Cloud

PROBLEM DEFINITION

PROBLEM DEFINITION
I
recommend
you

PROBLEM AND A (POSSIBLE) SOLUTION

PROBLEM AND A (POSSIBLE) SOLUTION
I recommend you Cloud Atlas
because you often like films with
Tom Hanks as Saving Private Ryan
and Da Vinci Code. In addition, you
sometimes like films directed by
The Wachowskis as The Matrix.

EXPLOD: THE INTUITION
➤ connecting user preferences and
recommendations through a set of
LOD-based properties

ITEM ATTRIBUTES: ENTITY LINKING

EXPLOD: THE FRAMEWORK
Mapper Builder
Ranker Generator
Proﬁle Recommendations (potentially any algorithm)
LOD-aware
represent.
Top-N
properties
Graph Data Model

Mapper Builder
Ranker Generator
Proﬁle Recommendations
LOD-aware
represent.
Top-N
properties
Graph Data Model
represents user proﬁles
and recommendations as
DBpedia nodes
http://dbpedia.org/
page/Cloud_Atlas_(film)
http://
dbpedia.org/page/
Saving_Private_Ryan

Builder
Ranker Generator
Top-N
properties
builds a graph-based data
model that exploits the
information in the LOD cloud
Mapper
LOD-aware
represent.
Graph Data Model
American Epic Films
Tom Hanks
Dystopian Films
The Wachowskis
Saving
Private Ryan
Da Vinci Code
The Matrix
Cloud Atals
Items in the
user profiles (Ip)
LOD-cloud properties (P)
Recommended
Items (Ir)
dcterms:subject
dcterms:subject
dcterm
s:subject
dcterms:subject
dbpedia-owl:starring
dbpedia-owl:director

Mapper Builder
Ranker Generator
LOD-aware
represent.
Top-N
properties
Graph Data Model
selects properties which
explain the recommendation
c = property
Ip = items in the user profile
Ir = recommendations
nc, Ip = # edges connecting c to items in Ip
nc, Ir = # edges connecting c to items in Ir
IDFc = adaptation of IDF for c
Map
Pro Recomme
LOD-aware
Graph
C1
C2
C3
C4
Ip1
Ip2
Ip3
Ir1
dcterms:
dcterms:
dcterm
s:
dcterms:dbpedia-
dbpedia- dbpedia-
dbpedia-
dbpedia-

Mapper Builder
Ranker Generator
LOD-aware
represent.
Top-N
properties
Graph Data Model
generates the natural
language explanation

Mapper Builder
Ranker Generator
LOD-aware
represent.
Top-N
properties
Graph Data Model
generates the natural
language explanation
I recommend you Cloud Atlas
because…

EXPLOD@WORK

EXPLOD@WORK
I recommend you Cloud Atlas because

EXPLOD@WORK
Tom
Hanks
I recommend you Cloud Atlas because you often like films with Tom
Hanks as Saving Private Ryan and Da Vinci Code.
dbpedia-owl:starring dbpedia-owl:starring

EXPLOD@WORK
Tom
Hanks
The
Wachowskis
Hanks as Saving Private Ryan and Da Vinci Code. In addition, you
sometimes like films directed by The Wachowskis as The Matrix.

EXPLOD@WORK
American Epic Films
Tom
Hanks
Dystopian Films
The
Wachowskis
Hanks as Saving Private Ryan and Da Vinci Code. In addition, you
sometimes like films directed by The Wachowskis as The Matrix.
dcterms:subject!
dcterms:subject!
dcterm
s:subject!
dcterms:subject!

EXPERIMENTAL EVALUATION
User study
308 subjects
➤ male = 70%
➤ medium interests in movies
➤ 90,318 movies from IMDB

4 explanation styles
➤ popularity (baseline)
➤ LOD properties
➤ ExpLOD
➤ ExpLOD no IDF

Metrics
➤ Transparency
I understood why I received the recommendation
➤ Persuasion
The recommendation is more convincing
➤ Engagement
I discovered new information about the movie
➤ Trust
My trust in the recommender is increased
➤ Effectiveness
I like the recommendation

Research Question 1
Is ExpLOD able to lead to more transparent, persuasive,
trustful, or engaging recommendations?
Research Question 2
Is there a correlation between the properties used to
generate the explanation and the effectiveness of
the explanation itself?

user asked to
➤ provide basic demo data
➤ provide preferences
➤ read the explanation and
answer to 5 questions
➤ express a rating before
and after watching the
movie trailer

Research Question 1
Is ExpLOD able to lead to more transparent,
persuasive, trustful, or engaging recommendations?
YES
transparency persuasion engagement trust effectiveness
popularity 3.01 2.59 2.31 2.67 0.93
LOD prop 3.04 2.84 3.28 2.81 0.66
ExpLOD-noIDF 4.00 3.39 3.48 3.39 0.72
ExpLOD 4.18 3.41 3.31 3.36 0.75
The best-performing conﬁguration is in bold. Signiﬁcant improvements over the baselines are in
italics.
Transparency, persuasion, engagement, and trust are evaluated by the average score collected
through the questionnaires. 
Effectiveness is calculated as the normalized difference between pre- and post-trailer ratings

Research Question 2
Is there a correlation between the properties used to
generate the explanation and the effectiveness of
the explanation itself?
YES
aim question positive negative
transparency I understood why I received the
recommendation
topic, director
distributor, music
composer
persuasion The recommendation is more convincing awards, director location, producer
engagement
I discovered new information about the
movie
writer, director producer, distributor
trust My trust in the recommender is increased awards, composer producer, topic
effectiveness I like the recommendation director, writer location, composer

QUESTIONS?
Thanks for your attention!
fedelucio.narducci@uniba.it
BPN

ExpLOD: a framework for explaining recommendations based on the Linked Open Data Cloud

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