This document summarizes research on explanations in recommender systems. It discusses the goal of using explanations to increase the performance of recommendation agents. It then categorizes the different types of explanations, including context-based, example-based, social, and content-based explanations. Finally, it outlines some open challenges in the field, such as generating personalized natural language explanations and evaluating explanations across different domains and recommendation techniques.

1. EXPLANATIONS IN
RECOMMENDER SYSTEMS
Overview And Research Approaches
Mohammed Zuhair Al-Taie
AL-Salam University College
-- Iraq –
Email: mza004@live.aul.edu.lb
This study was published in “The International Arab
This study was published in “The International Arab
Conference on Information Technology (ACIT)” December
Conference on Information Technology (ACIT)” December
-2013
-2013

2. Goal 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.
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3. 3

4. 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.
Benefits
 RS are being well used in various application
domains such as music, videos, queries, news,
friends on social networks etc..
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5. Amazon’s Recommendation System
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6. Explanations 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
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7. Explanations - Examples
YouTube
Explanation System
AR
estaurant
Recommendation Explanation
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8. Explanations - Example
Amazon
Explanation System
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9. ?Why Using Explanations in RS
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10. Phrases Expressing Explanations
 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
…
…
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11. Explanations – A Short History
 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.
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12. …However
 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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13. Types of explanations in RS
 Different criteria to classify
explanations …
13

14. RS Explanation Styles
 Explanation styles are related to the methods
used to generate explanations.
 The most commonly-used explanation styles are:
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15. 15

16. Research Approaches in Explanations
 Researchers spread their efforts across different
research aspects. Generally, they can be divided
into two approaches:
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17. 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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18. .(Explanations Attributes (Cont
Transparency- 1
Provide information so the user can comprehend the reasoning
used to generate a specific recommendation
Validity- 2
Allow a user to check the validity of a recommendation
Trustworthiness- 3
A mechanism for reducing the complexity of human decision
making in uncertain situations
Persuasiveness- 4
Persuasive 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
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19. .(Explanations Attributes (Cont
Scrutability- 5
means that users can tell if the system is wrong
Effectiveness- 6
Help users make better decisions
Efficiency- 7
Reduce the decision-making effort
Reduce the time needed for decision making
Satisfaction- 8
Improve the overall satisfaction stemming from the use of a
recommender system
Relevance- 9
Explanations can be provided to justify why additional
information is needed from the user
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20. .(Explanations Attributes (Cont
Comprehensibility- 10
Recommenders 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
Education- 11
Educate users to help them better understand the product
domain. So, as customers become more informed, they are
able to make wiser purchasing decisions
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21. Other Research Directions
Other than explanation attributes, researchers are
investigating a number of different approaches. Among
them are the following three important fields:
1. Explanation Interfaces
2. Decision Making
3. Over and Under Estimation
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22. Explanation Interfaces( 1
 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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23. Decision Making( 2
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24. Over and Underestimation( 3
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.
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25. Open Challenges
A number of challenges are still waiting to be probed by
people working in the field:
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26. !THANK YOU