Recommendation systems play a crucial role in allowing users
to navigate efficiently the vast number of options and information
that is currently available.
Nevertheless, these systems are mostly known
for their services and impact on individual users, often
neglecting the possibility of picturing their services for groups of people.
To fill this gap, designers and practitioners are increasingly
implementing the systems in various cooperative and social computing domains.
The Groups of Recommendation Systems often considers the individual preferences
of each member of the group, their social and behavioral aspects,
among other clues to calculate good algorithmic recommendations,
despite the different levels of heterogeneity within the group.
In this context, previous research has reported how personalized
recommendation systems produce the negative experiences for their users,
such as invisibility, anxiety and panic, bias in personalization
processes, lack of control and meaningful feedback, low algorithmic awareness,
and a long list of ethical issues such as discrimination, and challenges
to user autonomy.
However, to our knowledge, there is no comprehensive meta-analysis
that takes a human centric stance
to determine interaction design strategies for Group Recommender Systems,
focusing on the specific problems associated with these systems, such
as transparency, justifications, control,
diversity, group formation or achieve a final group consensus.
For this reason, we decided to provide such a systematic review,
offering designers and professionals a necessary outlet to address these issues
and imagine new ways to interact with Group Recommendation Systems.
Our systematic selection process resulted in
a final corpus of 142 academic products.
Following a systematic coding process for the corpus, we used
descriptive statistics and thematic analysis to identify current
trends and gaps in scholarly research regarding our research questions.
Based on our findings, we propose a comprehensive typology containing 28
different interactive design strategies for Group Recommendation Systems
in six different areas: user and group profiles transparency,
justification of the recommendation, control, diversity and serendipity,
group formation, and achieving a final group consensus.
The first area is user/group profile transparency.
This variable brought five different interaction design strategies
where the system represents the profile of the user or group.
The first one is feature-based rating, that shows the current
weight of each characteristic considered to create the recommendations.
The second is Rrecommended item-based rating, that exposes
the current weight of each recommended item to be included as a recommendation.
The third is based on iterative criticism that shows how the members of the group
have iteratively criticized each of the recommended elements.
The fourth is based on visualization.
The interface chose different visualizations
that represent the current preferences to create the recommendations.
And the fifth is based on keywords.
Where the system has
a predefined set of keywords to represent the user or group profile.
The second area is justification of the recommendation.
This variable showed three interaction design strategies.
The first one is text-based justification, the next one is
the coordinator-based justifications, where the system allows
the user to justify each recommendation manually,
and the last one is justifications based on visualizations,
the system justifies each recommendation with graphs and visualizations.
Our third area is control.
This variable is divided into two main groups:
The first group is control over recommendations.
The system allows the user to control and offer
feedback on the recommended articles.
Here, we found three strategies that the system offers to control:
by number ratings: by binary ranking, for example,
with a binary yes or no system or like and dislike:
and the third one is by ranking, where the user is able
to rank the recommendations in ordered sequence of likelihood.
The second group is control over user/group profile.
The system allows the user to control the user or group profiles,
changing their preferences or current weights to create the recommendations.
Here, we find three strategies where the system offers to control
in different ways.
By personal restrictions: were the system follows personal preferences
for the characteristics of the recommendations.
By filtering restrictions:
were the system adds filters to the possible recommendations.
By weight of the members of the group: the system offers to control
how relevant each group member’s profile is to produce the recommendations.
And the last one is by using two or more areas of the previous control strategies.
The next area is diversity and serendipity.
We found only one interaction design strategy,
that is by Recommendation filtering based on unknown elements
that allow users to explore their options besides the usual consumed elements.
Our fourth area is group formation, and it contains six interaction
design strategies that allow users to form their groups:
We have a social media strategy, where the system uses
a social media platform to invite other group members.
The second strategy is the admin-centered: where one admin user
handles all group formation, features and decisions.
Opposite to this one, is the horizontal Strategy,
where all users handle group formation and decision.
The fourth one is discoverable team strategy,
where users can find groups and join them.
Another one is the automatic decision strategy: the system
automatically creates the group of users based on their preferences.
And the last one is other strategies: where the system uses a diverse
set of options to form groups, like tag reading,
logging in simultaneously, face recognition,
QR codes, login and others.
Finally, the last area is Achieving a Final Group Consensus.
The interaction design approaches we discovered in this group, allowed users
to achieve a final decisions about the recommended items.
These ones are summarized as follows: democratic approach: all users vote
for their preferred recommendation and the most voted option wins.
Veto power approach:
a user has the last word to choose between the recommended items.
Mutual agreement approach:
where the system provides features for users to discuss
on their preferred items until group members reach a mutual agreement.
Third party approach: where each member of the group delegates the final decision
on the recommended elements to a third party.
Filtering and classification approach: where users can filter
by reducing the number of recommendations until the group reaches a final decision.
Finally, a review also identified several opportunities to explore Group
Recommender Systems and its implementation in novel contexts.
We break these opportunities down into application domains and devices.
We found that many articles deal
with tourist routes or point of interest group recommendations
only deal with leisure activities,
but there are no studies dedicated to exploring route
recommendations for more serious purposes, such as commuting
or work-related route recommendations.
We also found no approach for tangible user interfaces
or more material ways to interact with group recommendations,
offering a new approach to study this research context.
In addition,
we believe
that there is still a research gap that invites us to systematically explore
current analysis and research methods that include users
in the Group Recommendation System interface design process.
Considering that 72% of the academic
products of our final corpus reported, at least one user a study
with a strong tendency towards quantitative and behavioral approaches,
it seems urgent to encourage
the active inclusion of users to design the interaction of recommender systems
with various analysis, methods and research.
Lastly, we see an opportunity to include more qualitative approaches
that could expand current notions about Group Recommendation Systems,
interaction and uncover new areas of design.
A human-centered approach that engages users early in the design
process must be the norm for this domain to achieve proper interfaces.