Recommender systems (RS) have become an integral component of our daily lives by helping decision making easier for us. The use of recommendations has, however, increased the demand for explanations that are convincing enough to help users trust the provided recommendations. The recommendations are desired by the users to be understandable as well as personalized to their individual needs and preferences. Research on personalized explainable recommendation has emerged only recently. To help researchers quickly familiarize with this promising research field and recognize future research directions, we present a multi-dimensional conceptualization framework for personalized explanations in RS, based on five dimensions: WHAT to personalize?, TO WHOM to personalize?, WHO does the personalization?, WHY do we personalize?, and HOW to personalize?. Furthermore, we use this framework to systematically analyze and compare studies on personalized explainable recommendation.
users’ ratings and likes or dislikes, as compared to the items consumed by similar users.
Over the past few years, with the increased usage of However, the majority of RS still act as a black-box online services like social media, e-learning, and e- and users have no idea why and how items are being commerce, recommender systems (RS) have become an recommended to them. Therefore, it is increasingly integral part of our lives. These RS help in shaping the important to make RS more intelligible and investigate decisions of users and helping them choose what they methods to explain them to end-users. Explaining the want based on a number of relevant options presented reasoning behind a recommendation has become an acto them, called recommendations. However, with the tive area of research in the last few years. Researchers increased amount of available recommendations, there have argued that explanations in RS could be very benis a chance of creating mistrust among users about the eficial [ 1, 2, 3]. To “explain” means “to make known, presented information. A huge amount of available in- to make plain or understandable, to give the reason formation creates ‘information overload’ which might for or cause of” [4]. An explanation seeks to answer lead to users questioning the validity of the provided questions such as what, why, how, what if, why not, content and might think of it as misinformation. and how to [5]. Providing the reasoning behind why
One way to overcome this challenge is to provide per- an item is recommended to the user or how the
recsonalized recommendations to the users. The content ommendation process works, as an explanation, adds
of these recommendations is adapted to users’ inter- to the system’s transparency [6] and can benefit user
ests and only relevant items are recommended to them. experience and trust in the RS [1] .
The goal of personalized recommendations is to pre- Explainable RS have traditionally followed a
onedict items considered attractive and interesting by the size-fits-all model, whereby the same explanation is
user. This relevant item prediction is made by either provided to each user, without taking into
considera(1) content, i.e., items having similar content with the tion an individual user’s context, i.e., abilities, goals,
items already used by the user are recommended or (2) needs, or preferences. In the explainable
recommendapast behavior, i.e., items are recommended based on tion field, research regarding personalized explanation
has emerged only recently, showing that personal
charJHoeilnstinPkrio,cFeiendlianngds of the ACM IUI Workshops 2022, March 2022, acteristics have an impact on the perception of
expla$ qurat.ain@stud.uni-due.de (Q.U. Ain); nations, and that there is potential for the development
mohamed.chatti@uni-due.de (M.A. Chatti); of personalized explanations in RS [7, 8]. For example,
mouadh.guesmi@stud.uni-due.de (M. Guesmi); researchers have focused on investigating what specific
shoeb.joarder@uni-due.de (S. Joarder) characteristics may play a role in a user’s interaction
with an explainable RS [
The paper is structured as follows. We first outline In the field of explainable recommendation, research
the background for this research (Section 2). We then regarding personalized explanation is emerging,
recpresent the details of the proposed conceptualization ognizing that it is increasingly important not only to
framework (Section 3) and use the framework to ana- explain recommendations to the user but also to
perlyze the literature on personalized explainable recom- sonalize these explanations [7, 8]. Studies showed that
mendation (Section 4). Finally, we summarize the work diferent users have diferent reactions to, and
expecand outline future research plans (Section 5). tations from explainable RS [17, 9] and that personal
characteristics play a major role in the perception of,
and interaction with these systems [
In the field of explainable AI (XAI), Mohseni et al. [11] argue that diferent user groups will have other goals in mind while using such systems. For example, while machine learning experts might prefer highly-detailed visual explanations of deep models to help them optimize and diagnose algorithms, lay-users do not expect fully detailed explanations for every query from a personalized agent. Instead, systems with lay users as target groups aim to enhance the user experience with the system through improving their understanding and trust. In the same direction, Miller [12] argues that providing the exact algorithm which generated the specific decision is not necessarily the best explanation.
Therefore, the literature on AI/ML in recent years has emphasized the need for explanations that are tailored to individuals, i.e., personalized explanations. For example, Arya et al. [13] stressed that one explanation does not fit all, as diferent AI stakeholders present diferent requirements for explanations and may desire diferent kinds of explanations (e.g., feature-based, instancebased, language-based). The authors presented an AI toolkit, which contains eight state-of-the-art explainability algorithms that can explain an AI model in different ways to a diverse set of users. Jung and Nardelli [14] pointed out that XAI is challenging since explanations must be tailored (personalized) to individual users
To dive deeper into the understanding of key concepts
related to personalized explanation in RS and provide
a systematic categorization of the literature in this
area, we propose a multi-dimensional
conceptualization framework for personalized explanations in RS
(see Figure 1). To develop this framework, we gathered,
utilized, and adapted ideas, concepts, and methods
related to personalized explanations in the RS literature
and formulated a succinct and concise framework based
on five dimensions: WHAT to personalize?, TO WHOM
to personalize?, WHO does the personalization?, WHY
do we personalize?, and HOW to personalize?. Similar
to the conceptualization of personalized explanation
in ML presented in [16], we adopted and adapted the
framework presented by Fan and Poole [
The "WHAT" dimension refers to the properties of an There is a large design space for explainable RS. Reexplanation that can be adjusted to the user profile searchers presented diferent ways to design explainto provide personalized explanations. We identified able RS, referred to as explanation design choices [17, two main explanation properties that can be adapted 21]. Like the content of an explanation, these design based on explainee (for whom explanations are pro- choices represent further characteristics of an explavided) data, namely content and design choices of the nation that can be customized based on a user profile. explanation. Based on the literature on explainable recommendation, we identified five explanation design choices, namely 3.1.1. Explanation Content explanation style, explanation scope, explanation format, level of detail, and intelligibility types.
Content of an explanation refers to the information Explanation Style: The explanation style refers presented in an explanation. This information repre- to the model or strategy used for generating explanasents a description of details related to the recommen- tions [3]. In general, the explanation style is dependent dation process. These include, for example, user/item on the recommendation approach used in the RS, e.g., attributes contributing to the recommendation, inner a content-based RS produces content-based explanaworkings of background algorithms, information re- tions [2]. In case of complex RS (e.g., deep learning lated to the user model used as input to the RS, simi- models), however, the explanation style for a given larities between users and/or items, and connection be- explanation may not reflect the underlying algorithm by which the recommendations are computed [2, 3]. image, a graph, or a chart (visual). Textual explanaPersonalizing the explanation style means to present tions are usually in form of short or long sentences explanation in diferent styles to diferent users based using verbal elements, i.e., words, phrases or natural on their preferences. Explanation styles have been per- language describing the reasoning behind a recommenceived diferently in diferent domains [ 22, 23]. In this dation. Visual explanations are usually in a graphical paper, we build on the taxonomy of explanation styles format using visual elements to explain a generated in RS used in [2, 9, 24]. recommendation. Personalizing the explanation format means to present the explanation in the format • User-based Explanations: Explains similarity with preferred by the user.
other users having same tastes. For example: Level of detail: The level of detail refers to the User A with whom you share similar tastes, likes amount of information exposed in an explanation that item B. should be presented to a user [17, 11]. Users are not • Item-based Explanations: Explains recommended always interested in all the information that is proitems based on item (rating) similarity. For ex- duced in an explanation [12]. Diferent users demand ample: People who like item A in your profile diferent levels of explanation information and explaalso like item B. nations may cause negative efects if an explanation is dificult to understand [ 26]. Thus, it is important • Content-based Explanations: Explains similarity to provide explanations with enough details to allow between item features. For example: Item A has users to build accurate mental models of how the RS similar features as of item B purchased by you. operates without overwhelming them. For example, in an explanation, providing the exact algorithm that • Social-based Explanations: Explains similarity be- was used to generate the explanation may be a good tween users who know each other. For example: choice for a Machine Learning (ML) expert but not for Your friend User B likes item A. a lay user [12]. Furthermore, the demand for level of • Knowledge-based Explanations: Explains the de- detail in an explanation also varies with the cognitive scription of user’s needs or interests in the con- abilities of the user [26]. Only when the users have text of a recommendation. For example: This des- enough time to process the information and enough tination has higher average temperature, which ability to figure out the meaning of the information, is better for sunbathing. a higher level of detail in the explanation will lead to a better understanding. But as soon as the amount of • Demographic-based Explanations: Explains the information is beyond the users’ comprehension, the use of demographic data and its connection to explanation could lead to information overload and the recommendations. For example: This movie bring confusion [17]. was recommended to you according to your age. Intelligibility Types: When user encounters a complex system, she might demand diferent type of ex
Explanation Scope: The explanation scope refers planatory information based on the system and its conto the part of RS that an explanation focuses on, i.e., text [11]. Lim and Dey [5] identified several queries input, process or output [25]. Explanation focusing on (called intelligibility types) that a user may ask of a input tries to explain the user model which is taken smart system. These include: as an input by the RS. Explanation focusing on process tries to explain the working and flow of the underlying • How Explanations: demonstrate how the underalgorithm used to generate a recommendation. Expla- lying system behind a recommendation works. nation focusing on output tries to explain why an item How explanations are useful when Users are inwas recommended. Diferent users demand explana- terested in knowing how the system generates tions with a diferent scope. Not every user is interested certain recommendations. How explanations in knowing the details of the user model or the inter- aim to answer the question: "How (under what nals of the underlying algorithm [11]. Personalizing conditions) does the system do Y?". the explanation scope means to present explanations that focus on the input, output or process, according • Why Explanations: demonstrate why a recomto the user’s preferences and needs. mendation is made for a particular user. These
Explanation Format: The explanation format refers explanations cover what was the input to the to how an explanation is displayed to the user. It can system (user model) and what logic was used to either be in form of a text description (textual) or an generate the recommendation. Why questions are very common by the user hence it is an es- 3.2.1. Target users sential intelligibility requirement. Some users might expect a very informative answer from this why explanation and a simple explanation may not satisfy them [5]. Why explanations aim to answer the question: "Why did the system do X?".
The explanations can be personalized for target users at diferent levels of granularity. A target user for an explanation can be an individual user, category of individuals (e.g., experts, lay users), or group of individuals (in case of group RS). • Why Not Explanations: (also called Contrastive 3.2.2. User model attributes
Explanations) help the users in understanding why a specific item was not recommended to To personalize the explanations for a user or a group of them. Lim et al. [27] argued that these explana- users requires to tailor explanation based on user modtions are useful for high-risk circumstances and els. Schneider and Handali [16] summarize diferent when user might ask for alternative possibilities user model attributes that can be used to customize an from the RS. Why Not explanations aim to an- explanation. These include: swer the question: "Why did the system not do Y?".
• Prior knowledge: What an explainee knows, e.g., knowledge about the RS domain, expertise regarding the RS methods to be explained • What Íf Explanations: deal with the manipulation of inputs to the RS. These explanations illustrate how the manipulation of inputs afect the output of the RS, i.e., recommendations. These explanations involve users’ interaction with the system when they can change an input to the RS and want to know what will happen as a consequence.
What If explanations aim to answer the question: "What If there is a change in conditions, what would happen?".
• Preferences/Interests: What an explainee likes and
prefers, e.g., preferred presentation format
(visual or textual), desired level of detail of the
explanation, the time or efort an explainee wants
to invest to understand the explanation. User
preference and interests are used
interchangeably in the literature on RS
• Decision information: What information is used
by an explainee to make decisions
• What Else Explanations: demonstrate diferent
examples of the similar inputs to the RS that can • Purpose: What the explanation is used for
produce similar outputs (recommendations). Lim
and Dey [5] demonstrated that although the de- Recent studies on explainable recommendation showed
mand for these explanations is low but these are that personal characteristics have an efect on the
perhelpful in critical situations when users expect ception of explanations and that it is important to take
that the RS is doing more than shown to them, personal characteristics into account when designing
to handle a critical situation. What else explana- explanations. Examples of personal characteristics that
tions aim to answer the question: "What else the can be used as further user model attributes
accordsystem is doing?". ing to which an explanation can be tailored include
the Big Five personality traits: openness,
conscien• How To Explanations: (also called Counterfactual tiousness, extraversion, agreeableness, and neuroticism
Explanations) are basically explanations about [28, 29, 30, 31, 32], need for cognition (NFC), [
The "TO WHOM" dimension focuses on to which user(s) User data collection indicates how data is collected to to personalize. A crucial requirement in explainable generate a model of the user for whom an explanation RS is to build detailed user models that can be used by is personalized, in our case the explainee. This could the system to recommend items or to provide personal- be same as the data used to generate recommendations ized explanations. These user models are based on data or diferent. There are two ways to get explainee data collected from the user to generate diferent attributes. to generate user models: • Implicit data collection: refers to getting explainee There are possible refinements to these goals. For examdata to generate a user model, implicitly through ple, in [1] satisfaction is not considered as a single goal, user’s past activity, browser search history, mouse but is split into ease to use, enjoyment, and usefulness. clicks, social media information, system usage Other explanation goals in RS include user engagement history, previously consumed items, etc. This re- resulting from more confidence and transparency in quires techniques like preference elicitation and the recommendations [34], compliance with legal reguknowledge extraction from raw data. lations e.g., European Union’s GDPR [7], education by allowing users to learn from the system [8], debugging • Explicit data collection: refers to getting explainee to be able to identify wrong or unexpected recommendata to generate a user model, explicitly by ask- dations and take control to make corrections [8]. This ing the user to write reviews and feedback to goal is closely related to scrutability [1]. The goal might items, giving ratings, filling out questionnaires, also be seen as obtaining an answer to diferent intelliinterviews and surveys, liking and disliking items gibility types of explanations, e.g., what, how and why etc. questions [27, 12].
We consider these goals as also important in
rela3.3. WHO does the Personalization? tion to personalized explanation and we use them as
possible candidates for the "WHY" dimension in our
The "WHO" dimension focuses on who does the person- conceptualization.
alization. The literature on personalized systems
distinguish between automatic personalization by the system
providing explanations (i.e., system-driven personalized 3.5. HOW to Personalize?
explanation) and manual personalization which is done The "HOW" dimension refers to the methods used to
on-demand by the explainee, actively setting the expla- generate personalized explanations. In general,
pernation parameters, e.g., choosing the level of detail to sonalized explanations can be created using a two-step
be shown in an explanation (i.e., user-driven personal- process, namely (1) adjusting explanation properties and
ized explanation) [
The "WHY" dimension refers to the intended goals of personalizing an explanation. Diferent users have diferent goals when they interact with an explainable RS. Thus, the explanation presented to a specific user should be personalized in a way to achieve the specific goal(s) intended by the user. Prior work on explainable recommendation has presented diferent explanation goals. For example, Tintarev and Masthof [2] identified seven goals, as follows: the extent to which an explanation describes all of the underlying system [36, 37]. The intelligibility type can be personalized by adjusting the query that a user can ask from the RS (e.g. how, why, why not, what if, what else, how to). Finally, the choice of the explanation style, e.g., user-based, item-based, content-based, social explanation can also be adjusted.
We used our conceptualization framework to systematically categorize and compare the literature on personalized explainable recommendation. Our aim was not to conduct a systematic literature review, but rather to show how the framework can be applied to analyze 3.5.2. Applying adaptation rules studies in the this research area. To identify relevant A system-driven personalized explanation requires to works, we focused on recent studies that explicitly adadjust explanation properties by taking into account dressed personalized explanation in RS. The results of users’ preferences and personal characteristics. Thereby, the categorization of these studies are summarized in it is crucial to decide which adaptation to apply and Table 2. then apply that adaptation [38]. This is a straightforward task in case of personalizing the content as an 4.1. WHAT to Personalize? wwTliittrsscaeebdbpmotsiieohhxxnyeeoheooiiwuqngopsptastrmnneohhhiuswtoalullgsilarpaaiisrlionelcgaenleinrua.nntdozndaserhiennHtlaaliowulgeobdlnttdftetdinoslheowoieisggeto,oofgowaoesrhdochMpemnnudlppooertqw,rerfeieihavfnaop(toudrndnsnh2oreieitrdrveeiigsnuth)nigrofelsuicicdit,imedpjrnetsspionhkcintiecsiserfehetogbtmneecnasodureorunniseh.ottesvsttisirsittylowdtn“eoiltaieaohued,iowsiertlnrroclavd.wefinfweaiuet,hsnt[etideyetsbeih1scihal,heuxgcspserh9denaeishpdieerc]wroaislaedsilspb?ehlfsiaetfd.peol”neou(esndpta.xeulAu3tshgnaeatepxwpTsstl)osygegctdrautpeloiepaasieiineeropladnlneonstaraexvcndpebingtgnonacaeoaaeaoevwhatdeaenlmtdupoifrulalixitoosthetdtfeirtaspcponeaormhuviekauthllcertnedeaelaspisahedieeostseanenerrnstexosen(dxraohaotst1cpirisxhcpatpsiginfa)iilsgptpplasweonasfaef,anlotrtoroennadrainiisetrrtirdtnncspaayuoafsdhhioeuutg.paptmftgesiiirotssTtllpgorcontriioiaueeiosheehenmencot-rrws.snrdodeeesssstt--- iittttt"rcegSdbppupmszhuaouLeoxitalleseebaaegacearaepptnrtdnyneiaoosrlsdshletltit,]atadiofmeezrreecit.fontexannnfsetyoxtoimmhnrodtahtvogtanpritatanenepiolye’toliienitwtsaateaosnrznpfxuefilnnelnetdoifdseempiarffhttoadzasredraelhn.isetera)matnrtt(iAboatdnimreycReotshoyfyehs.oqaxlni"SgbneaedontetnBupS,.mywht,iau,dsaolceaio"sreaiirttriokhaWnclnctenaecnegerraptenosnmaloaakokmmue.ybrHpettsft,oripeIr.sietinkofiAtndtafileoefsiTt,eheh.sgenechnTnaf"gthte[aeFrkgohmt"uceite[soeiuracnsuaensastdtmroynerhutrreasl9eiepnxlnetderesdma]r[asapest]xtt.aUrti,ceHwih)hrleee,alInlcnas2s,nnewesnilmaeeonoetk]tstnpslrrhdrhtvanlieplioievhdydsaeesteiosalaflceiieraeslaonlopnsseht[sltii,toafisinexggzgheemeuosfareep[needoxctesfsr1xddnliioopfexoeanle6coiterarnasnblnohx]etprtueasrtmhtyaaaeapoui]n’irpentliecltn"liptiorlneovieachcgz,eoev[ltrftraenoeeiee[v3onesualnrscdnaCsdi4iorcnsysdot,tetc(,]oodneeetiitituwaaeeonr.rhmlhulaotrtelpld’xangsodaeelieesr.-------,, the explainee.
All reviewed studies focused on personalizing the content of the provided explanation. For example, Gedikli et al. [40] presented and discussed the results of a user mation available in the Linked Open Data (LOD) cloud. study where recommendation systems were provided Their user study results revealed that their strategy outwith diferent types of explanation. The study revealed performed both a non-personalized explanation basethat the content-based tag cloud explanations were line and a popularity-based one. McInerney et al. [34] efective and well accepted by the majority of users. presented a method (Bart) that combines bandits and Tintarev and Masthof [45] focused on personalized recommendation explanations. This method is able to feature-based explanations that described item features, jointly learn which explanations each user responds to tailored to the user’s interests. Musto et al. [42] pre- (personalized explanation), and learn the best content sented a framework for generating personalized natural to recommend for each user (personalized recommenlanguage explanations of the suggestions produced by a dation). The conducted experiments revealed that pergraph-based recommendation model based on the infor- sonalizing explanations and recommendations provides a significant increase in estimated user engagement. Lu textual explanation format by explaining the reasonet al. [41] presented a multi-task learning framework ing behind an explanation in natural language. Only that simultaneously learns to perform rating prediction few studies have used a visual explanation format. For and generate personalized recommendation explana- example, Kouki et al. [9] have used Venn diagrams and tion. They employed a matrix factorization model for static cluster dendrograms, Gedikli et al. [40] have used rating prediction, and a sequence-to-sequence learning tag clouds, and Quijano-Sanchez et al. [44] have used model for explanation generation by generating person- graphical representation of images to present explaalized reviews for a given recommendation-user pair as nations. Finally, none of the studies have worked on they consider user-generated reviews as explanations personalizing intelligibility types of explanations deof the ratings given by users. Inspired by how people pending on user profile. explain word-of-mouth recommendations, Chang et al. In summary, it can be observed that personalizing [39] designed a process, combining crowd-sourcing the content of an explanation to each user’s data and and computation, that generates personalized natural personality is dominant in the literature on explainable language explanations. And, Chen et al. [43] provided recommendation. By contrast, personalized explanapersonalized explanations visually by highlighting dif- tions that focus on tailoring a certain explanation deferent parts of an item based on user preferences. sign choice, such as explanation scope, format, or level
Considering the design choices (i.e., explanation style, of detail are under-explored and deserve more research
scope, format, level of detail, intelligibility types) which in the future.
could also be personalized based on user profiles, most
of the studies have kept design choices fixed in ex- 4.2. TO WHOM to Personalize?
planations. Only the work presented in [24] takes the
personalized explanation to the explanation style design Related to the "To WHOM" dimension, which identifies
choice level. The authors proposed a hybrid method of the target users of a personalized explanation (i.e.,
indipersonalized explanation of recommendations, which vidual user, category of individuals, or group of
individcombines basic explanation styles to provide the appro- uals), it has been observed that almost all the reviewed
priate type of personalized explanation to each user. studies have personalized for individual users. Only the
Based on this method, each user will be given an expla- study in [44] provided explanations targeting a group
nation adapted to what most impressed her (i.e., expla- of users. The authors argued that adding a social
comnation style which she prefers). Furthermore, only the ponent to explanations in group recommenders can
works in [
In all reviewed studies, there was no personalization erence of movies modeled from their activities with
related to explanation scope, explanation format, or in- the system. Data was also collected implicitly through
telligibility types. None of the studies has focused on users’ listening history to generate their music interests
varying these design choices depending on the user in [34], through users’ likes to generate their movie
profile. In terms of the explanation scope design choice, preferences [42], through users’ interactions, readings,
all reviewed studies have focused only on explaining brought and clicked books, to generate user models
the output of the RS (i.e., recommendation), none of based on preferences [24]. Furthermore, a user model
them has tried to explain the input of the RS (i.e., user was created by [
Only the work by Quijano-Sanchez et al. [44] gener- ever, they found that personalization was detrimental
ated a user model based on user preferences collected to efectiveness. Similarly, Tintarev and Masthof [45]
implicitly from users’ activities in Facebook as well investigated the impact of personalizing simple
featureas personal characteristics (e.g., cooperative, assertive) based explanations on efectiveness and satisfaction.
obtained explicitly through a personality evaluation They also reported that their personalization method
test to get users’ behaviors, and social information re- hindered efectiveness, but on the other hand increased
lated to friends and their preferences to generate per- the satisfaction with the explanations. More studies
sonalized explanations where each user will receive are needed to investigate the benefits of personalized
a diferent explanation of the group recommendation explanations compared to non-personalized variants
presented by the system. In general, it can be observed related to diferent goals. Furthermore, only two
studthat there is less focus on personal characteristics as ies investigated the efects of personal characteristics
a user model attribute that can be collected (explicitly on the perception of explanations in terms of
persuaor implicitly) and used to personalize the explanations. siveness [9] and confidence [
Related to the "WHO" dimension, we have observed
that only the works presented in [
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