Although personalization plays a major role in the development of recommender systems, the presentation of recommendations{and especially the way in which it can be adapted to suit the user's needs{ has received relatively little attention from the research community. We introduce a design space for personalizing the presentation of recommendations and propose several dimensions that should be a part of it. Moreover, we present our initial insights about possible interactive mechanisms as well as potential evaluation criteria. Our goal is to provide a systematic way of designing personalized recommendation content, which should prove bene cial for other researchers working on this topic. In the longer term, we are interested to investigate whether such personalized presentation implementations in uence the perceived trustworthiness of the recommendations.
Personalization is an important aspect of recommender systems (RS). It allows
websites and other Internet services to cater to individual tastes, interests, and
preferences. For many years, objective accuracy was considered one of the most
important criteria for ranking RS [
A relatively unexplored topic in the eld of RS is the personalization of the presentation of recommended items. Once user preferences have been elicited (either implicitly or explicitly), this information can be used not only to suggest personalized predictions, but also to customize the way in which they are presented to the user. Adapting the presentation to t the user's needs has the potential to open novel interaction possibilities for users and might provide useful insights into the way in which people interact with RS. Against this background, exploring the design space for the personalization of recommendations is a useful research endeavor and an important step towards the implementation of a prototype. The goal of this paper is to introduce a design space for personalizing the presentation of recommendations and to present the dimensions that comprise it.
The remainder of the paper is structured as follows: We discuss related work in Section 2, before proceeding to present the design space in Section 3. We subsequently introduce some preliminary interactive mechanisms and evaluation criteria. Finally, we discuss possible limitations and directions for future research in Section 4. 2
Personalization is well-studied in the eld of RS. Some of the main research foci
include deciding, for a given recommendation, what information to present, when
to present it, how much of it to present, and in what way. For instance, di
erent information modalities (such as various types of result lists or combinations
of text and images) have been compared to observe their e ect on the
persuasiveness of recommendations and on the users' satisfaction [
Many existing approaches to personalizing the presentation of
recommendations rely on explanations [
Previous research into design spaces for adaptive user interfaces highlighted
the importance of control over the adaptation algorithm and the importance
of adequate measures for user evaluation [
We identify the following dimensions that comprise the design space (Fig. 1): modality, salience, comparison functions, interactive control, explanations, and trust cues. Each of these is explained in further detail below.
The design space is meant to be applicable to numerous domains in which RS are commonly used. To facilitate understanding of the various dimensions, throughout this section we limit ourselves to using examples from the hotel booking domain. Hotel recommendations are interesting for several reasons. First, there is a higher risk associated with such choices{in comparison with movie recommendations, for example. Risk arises, on the one hand, from the fact that staying in a hotel typically costs a considerable amount of money. On the other hand, there is also the risk associated with the e ects of a wrong recommendation on the user's wellbeing. Second, the items in question have a reasonable set of attributes that should be considered. These can be classi ed into hotel features (e.g., location, price), room characteristics (e.g., bed size, number of electrical sockets), and services (e.g., complimentary breakfast, free Wi-Fi). Third, there is a large body of user-generated content, in the form of reviews, photos, tags, and ratings, that can be leveraged in the presentation. 3.1
Modality refers to the form in which the content of the recommendation is
conveyed to the user. Information can be presented using text, graphical symbols,
audiovisual means, or combinations thereof [
Salience denotes the range of presentation features that are used to draw
users' attention. Particularly relevant information, such as attributes in which a
person is interested, should be emphasized. Conversely, less important features
might be shown in a subtler manner or even hidden altogether (e.g., business
services for vacationers). Standard presentation layouts, such as category-value
tables, can become di cult to parse if they exceed a certain number of rows.
Similarly, altering the size and color of text or using animations to highlight
important aspects can lead to information overload if used excessively. Instead,
a RS might re-order the list of attributes such that those that the user considers
most important are displayed at the top [
Comparison functions help users evaluate item attributes and values across
di erent recommendations [
Interactive control comprises the mechanisms through which a user in uences
the output of a RS. The complexity of the underlying algorithms that are used
to generate recommendations has increased a great deal over the years. For this
reason, many users associate modern RS with \black boxes" [
Explanations allow users to discover supporting evidence for a presented
attribute or claim and are one of the more common methods for increasing
transparency in RS [
Trust cues are interface elements that allow the user to determine the
reliability of the presented information [
A promising approach for personalizing the presentation of recommendations is
to employ interactive mechanisms that support the user's decision making
process [
The content of recommendations is typically organized into sections, such as general description, listing of attributes, ratings, tags, reviews or comments, and photos. It is reasonable to expect that users' preferences extend also to the order in which these sections are presented. For example, one might consider user reviews more relevant than the owner-supplied description of the hotel. Hence, the RS could allow users to customize the various content sections to their liking. Furthermore, the system might also attempt to match users' expectations based on available context information.
Further interactive mechanisms could be developed to facilitate users' control over their own personalization pro les. Ideally, the system should not only provide the means for users to edit their pro les, but also to preview the e ect that a prospective change would have on the presentation of the recommendations. 3.3
Based on the dimensions presented above, it seems possible to devise methods for evaluating RS with respect to how strongly the di erent dimensions are perceived by users. We believe that the main criterion for evaluating user interfaces that implement the design space is their suitability with respect to the user's informational need. This may depend on several factors, such as the consequences of choosing wrongly (e.g., in terms of monetary costs or the user's wellbeing), the required level of detail (i.e. how accurate does the information need to be), and user characteristics. 4
Personalizing the presentation of recommended items using interactive mechanisms can lead to increased transparency and control over the recommendation process. Since both aspects are central to the issue of trust, this additional kind of personalization might increase the perceived trustworthiness of the recommendations. However, this will need to be investigated empirically.
A limiting factor for the design of such interactive and personalized presentations is the quality of the user data, such as elicited preferences, that is available to the RS. At the same time, many of the existing user models are not optimized su ciently to support this level of customization. Therefore, one of our planned directions for future research is to investigate how the user models commonly used in RS can be expanded to a ord personalized presentations of recommendations.
Although we decided to focus in this paper only on the presentation of individual recommendations, personalized presentations also make sense for the item sets (i.e. even before the user has selected a recommendation for closer scrutiny). We believe that the design space can be employed successfully in this situation, though a more thorough examination of this case is required.
Finally, we plan to validate the design dimensions presented in this paper and to develop a prototype implementation of the design space on top of an existing platform for hotel recommendations.
This work is supported by the German Research Foundation (DFG) under grant No. GRK 2167, Research Training Group \User-Centred Social Media".