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EXPLANATIONS IN
RECOMMENDER SYSTEMS
Overview And Research Approaches

Mohammed Zuhair Al-Taie
AL-Salam University College
...
Goal of the study
 The goal of this study to survey & comprehend the
main

streams

of

research

in

the

field

of

Exp...
3
What Are Recommender System (RS)?
 Also called Recommendation Systems, they are
software

tools

and

techniques

providi...
Amazon’s Recommendation System

5
Explanations in Recommender System
 Important pieces of information that are used by
both selling and buying agents, thro...
Explanations - Examples
YouTube
Explanation System
AR
estaurant
Recommendation Explanation

7
Explanations - Example

Amazon
Explanation System

8
?Why Using Explanations in RS

9
Phrases Expressing Explanations
 More on this artist …
 Try something from similar
artists …
 Someone similar to you al...
Explanations – A Short History
 The importance of explanations has been well
identified in pervious paradigms such as Exp...
…However
 There are many types of explanations and various
goals they can achieve.
 Goals such as: effectiveness, effici...
Types of explanations in RS
 Different criteria to classify
explanations …

13
RS Explanation Styles
 Explanation styles are related to the methods
used to generate explanations.
 The most commonly-u...
15
Research Approaches in Explanations
 Researchers spread their efforts across different
research aspects. Generally, they ...
Explanations Attributes (Goals)
 Explanation
attributes
are
the
benefits
that
explanations give to recommender systems. T...
.(Explanations Attributes (Cont
Transparency- 1
Provide information so the user can comprehend the reasoning
used to gener...
.(Explanations Attributes (Cont
Scrutability- 5
means that users can tell if the system is wrong
Effectiveness- 6
Help use...
.(Explanations Attributes (Cont
Comprehensibility- 10
Recommenders can never be sure about the knowledge of their
users. T...
Other Research Directions
Other than explanation attributes, researchers are
investigating a number of different approache...
Explanation Interfaces( 1
 Explanation Interface is the technique used to control the
format by which explanations are pr...
Decision Making( 2

23
Over and Underestimation( 3
Over and Underestimation: overestimation means that
users may try a product they do not end up...
Open Challenges
A number of challenges are still waiting to be probed by
people working in the field:

25
!THANK YOU
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Explanations in Recommender Systems: Overview and Research Approaches

Recommender systems are software tools that supply users with suggestions for items to buy. However, it was found that many recommender systems functioned as black boxes and did not provide transparency or any information on how their internal parts work. Therefore, explanations were used to show why a specific recommendation was provided. The importance of explanations has been approved in a number of fields such as expert systems, decision support systems, intelligent tutoring systems and data explanation systems. It was found that not generating a suitable explanation might degrade the performance of recommender systems, their applicability and eventually their value for monetization. Our goal in this paper is to provide a comprehensive review on the main research fields of explanations in recommender systems along with suitable examples from literature. Open challenges in the field are also manifested. The results show that most of the work in the field focus on the set of characteristics that can be associated with explanations: transparency, validity, scrutability, trust, relevance, persuasiveness, comprehensibility, effectiveness, efficiency, satisfaction and education. All of these characteristics can increase the system's trustworthiness. Other research areas include explanation interfaces, over and underestimation and decision making.

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Explanations in Recommender Systems: Overview and Research Approaches

  1. 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. 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. 2
  3. 3. 3
  4. 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.. 4
  5. 5. Amazon’s Recommendation System 5
  6. 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 6
  7. 7. Explanations - Examples YouTube Explanation System AR estaurant Recommendation Explanation 7
  8. 8. Explanations - Example Amazon Explanation System 8
  9. 9. ?Why Using Explanations in RS 9
  10. 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 … … 10
  11. 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. 11
  12. 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. 12
  13. 13. Types of explanations in RS  Different criteria to classify explanations … 13
  14. 14. RS Explanation Styles  Explanation styles are related to the methods used to generate explanations.  The most commonly-used explanation styles are: 14
  15. 15. 15
  16. 16. Research Approaches in Explanations  Researchers spread their efforts across different research aspects. Generally, they can be divided into two approaches: 16
  17. 17. Explanations Attributes (Goals)  Explanation attributes are the benefits that explanations give to recommender systems. These benefits fall into the following 11 aims: 17
  18. 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 18
  19. 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 19
  20. 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 20
  21. 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 21
  22. 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: 22
  23. 23. Decision Making( 2 23
  24. 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. 24
  25. 25. Open Challenges A number of challenges are still waiting to be probed by people working in the field: 25
  26. 26. !THANK YOU

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