EXPLANATIONS IN EXPLANATIONS IN RECOMMENDER SYSTEMSRECOMMENDER SYSTEMS
Overview And Research ApproachesOverview And Research Approaches
EXPLANATIONS IN EXPLANATIONS IN RECOMMENDER SYSTEMSRECOMMENDER SYSTEMS
Overview And Research ApproachesOverview And Research Approaches
Mohammed Zuhair Al-TaieMohammed Zuhair Al-TaieAL-Salam University CollegeAL-Salam University College
-- Iraq –-- Iraq – Email: [email protected] Email: [email protected]
This study was published in “The International Arab Conference on Information Technology
(ACIT)” December -2013
This study was published in “The International Arab Conference on Information Technology
(ACIT)” December -2013
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Goal of the studyGoal of the studyGoal of the studyGoal of the study
The goal of this study to survey & comprehend the
main streams of research in the field of
Explanations in Recommender Systems and put
them in one integral work.
It starts by explaining the main concepts of the
field and then moves on to present and discuss the
various sub-topics that took much interest from
researchers.
The goal of this study to survey & comprehend the
main streams of research in the field of
Explanations in Recommender Systems and put
them in one integral work.
It starts by explaining the main concepts of the
field and then moves on to present and discuss the
various sub-topics that took much interest from
researchers.
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What Are Recommender System (RS)?What Are Recommender System (RS)?What Are Recommender System (RS)?What Are Recommender System (RS)?
Also called Recommendation Systems, they are
software tools and techniques providing
suggestions for items to be of use to a user.
BenefitsBenefits
RS are being well used in various application
domains such as music, videos, queries, news,
friends on social networks etc..
Also called Recommendation Systems, they are
software tools and techniques providing
suggestions for items to be of use to a user.
BenefitsBenefits
RS are being well used in various application
domains such as music, videos, queries, news,
friends on social networks etc..
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Amazon’s Recommendation SystemAmazon’s Recommendation SystemAmazon’s Recommendation SystemAmazon’s Recommendation System
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Explanations in Recommender SystemExplanations in Recommender SystemExplanations in Recommender SystemExplanations in Recommender System
Important pieces of information that are used by
both selling and buying agents, through their
communication process, to increase their
performance.
Another definition … it is a description that makes
users better realize if the recommended item is
relevant to their needs or not
Important pieces of information that are used by
both selling and buying agents, through their
communication process, to increase their
performance.
Another definition … it is a description that makes
users better realize if the recommended item is
relevant to their needs or not
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YouTube Explanation System
YouTube Explanation System
A Restaurant Recommendation
Explanation
A Restaurant Recommendation
Explanation
Explanations - ExamplesExplanations - ExamplesExplanations - ExamplesExplanations - Examples
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AmazonExplanation
System
AmazonExplanation
System
Explanations - ExampleExplanations - ExampleExplanations - ExampleExplanations - Example
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Why Using Explanations in RSWhy Using Explanations in RS??
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More on this artist …
Try something from similar
artists …
Someone similar to you also
like this …
As you listened to that, you
may want this …
These two go together …
This is highly rated …
Try something new …
Similar or related products
…
More on this artist …
Try something from similar
artists …
Someone similar to you also
like this …
As you listened to that, you
may want this …
These two go together …
This is highly rated …
Try something new …
Similar or related products
…
Complementary
accessories ...
Gift idea ...
Welcome back (recently
viewed) …
For you today …
New for you …
Hot / Most popular of this
type …
Other people also do this
…
Complementary
accessories ...
Gift idea ...
Welcome back (recently
viewed) …
For you today …
New for you …
Hot / Most popular of this
type …
Other people also do this
…
Phrases Expressing ExplanationsPhrases Expressing ExplanationsPhrases Expressing ExplanationsPhrases Expressing Explanations
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The importance of explanations has been well
identified in pervious paradigms such as Expert
Systems.
Due to the decline of studies in Expert Systems in
the 1990s, Recommender Systems borrowed the
concepts of explanations.
A seminal study by Herlocker et al. in 2000 on
explanations in RS, which stated that
recommender systems had worked as black boxes,
lead the body of research in explanations to grow.
The importance of explanations has been well
identified in pervious paradigms such as Expert
Systems.
Due to the decline of studies in Expert Systems in
the 1990s, Recommender Systems borrowed the
concepts of explanations.
A seminal study by Herlocker et al. in 2000 on
explanations in RS, which stated that
recommender systems had worked as black boxes,
lead the body of research in explanations to grow.
Explanations – A Short HistoryExplanations – A Short HistoryExplanations – A Short HistoryExplanations – A Short History
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HoweverHowever… …
There are many types of explanations and various
goals they can achieve.
Goals such as: effectiveness, efficiency,
transparency, trustworthiness, validity.. can not
all be achieved in one system at one time.
Therefore, a deep understanding of explanations
and their effects on customers is of great
importance.
There are many types of explanations and various
goals they can achieve.
Goals such as: effectiveness, efficiency,
transparency, trustworthiness, validity.. can not
all be achieved in one system at one time.
Therefore, a deep understanding of explanations
and their effects on customers is of great
importance.
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Types of explanations in RS Types of explanations in RS Different criteria to classify
explanations …
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RS Explanation StylesRS Explanation Styles Explanation styles are related to the methods
used to generate explanations. The most commonly-used explanation styles are:
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Research Approaches in ExplanationsResearch Approaches in Explanations Researchers spread their efforts across different
research aspects. Generally, they can be divided into two approaches:
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Explanations Attributes (Goals)Explanations Attributes (Goals) Explanation attributes are the benefits that
explanations give to recommender systems. These benefits fall into the following 11 aims:
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1 -TransparencyProvide information so the user can comprehend the reasoning used to generate a specific recommendation
2 -ValidityAllow a user to check the validity of a recommendation
3 -TrustworthinessA mechanism for reducing the complexity of human decision making in uncertain situations
4 -PersuasivenessPersuasive explanations for recommendations aim to change the user's buying behavior. E.g., a recommender may intentionally dwell on a product's positive aspects and keep quiet about various negative aspects
Explanations Attributes (Cont.)Explanations Attributes (Cont.)
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5 -Scrutability
means that users can tell if the system is wrong
6 -EffectivenessHelp users make better decisions
7 -EfficiencyReduce the decision-making effortReduce the time needed for decision making
8 -SatisfactionImprove the overall satisfaction stemming from the use of a recommender system
9 -RelevanceExplanations can be provided to justify why additional information is needed from the user
Explanations Attributes (Cont.)Explanations Attributes (Cont.)
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10 -ComprehensibilityRecommenders can never be sure about the knowledge of their users. Therefore explanations support the user by relating the user's known concepts to the concepts employed by the recommender
11 -EducationEducate users to help them better understand the product domain. So, as customers become more informed, they are able to make wiser purchasing decisions
Explanations Attributes (Cont.)Explanations Attributes (Cont.)
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Other than explanation attributes, researchers are investigating a number of different approaches. Among them are the following three important fields:
Other Research DirectionsOther Research Directions
1. Explanation Interfaces
2. Decision Making
3. Over and Under Estimation
1. Explanation Interfaces
2. Decision Making
3. Over and Under Estimation
Explanation Interface is the technique used to control the format by which explanations are presented to a user (meaning that how explanations are shown to users).
Motives:
The importance of a good interface is that it can better explain recommendations and can even push users to make further requests.
The use of modalities such as text, graphs, tables, images and colors can better present explanations to users. For example:
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11 ( (Explanation InterfacesExplanation Interfaces
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22 ( (Decision MakingDecision Making
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Over and Underestimation: overestimation means that users may try a product they do not end up liking. underestimation means that users miss products they might have appreciated
Motives:
Overestimation may lead users later on to distrust the system after discovering that the items it recommended were not that useful. On the other hand under estimation may make users miss items that fitted their interests and eventually make them distrust the system.
33 ( (Over and UnderestimationOver and Underestimation
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A number of challenges are still waiting to be probed by people working in the field:
Open Challenges Open Challenges
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