Challenges and Solutions in Group Recommender Systems

CHALLENGES AND SOLUTIONS IN
GROUP RECOMMENDER SYSTEMS
Ludovico Boratto (ludovicoboratto.com – ludovico.boratto@acm.org)
Eurecat (Spain)
ICDM 2017 – 17th IEEE International Conference on Data Mining

Plan of the talk
1. Recommender systems principles
2. Group recommendation introduction
3. Tasks and state of the art survey
4. Evaluation methods
5. Emerging aspects and techniques
6. Case study
7. Summary

[Ricci et al. 2015]
Recommender systems principles

Jeff Bezos
¨ “If I have 3 million
customers on the Web,
I should have 3 million
stores on the Web”
¤ Jeff Bezos, CEO of
Amazon.com

Recommender systems
¨ Suggest items that might interest a user

Recommender Systems
¨ In everyday life we rely on recommendations
from other people either by word of mouth,
recommendation letters, movie and book reviews
printed in newspapers, ...
¨ In a typical recommender system people provide
recommendations as inputs, which the system
then aggregates and directs to appropriate
recipients

Recommender Systems
¨ A recommender system helps to make choices
without sufficient personal experience of the
alternatives
¤ To suggest products to their customers
¤ To provide consumers with information to help them
decide which products to purchase
¨ They are based on a number of technologies:
information filtering, machine learning, adaptive
and personalized system, user modeling, …

The recommendation problem
¨ We are given:
¤ a set of users
¤ a set of items
¤ a set of values (e.g., V=[1,5] or V={like,dislike})
¨ Let be a ternary relation that contains the
preferences given by the users
¨ We denote as the subset of items evaluated by a
user u
¨ The objective is to define a function
(prediction of the unknown ratings) and to identify an
item i* with the highest predicted rating:
U = {u1,u2,...,un}
I = {i1,i2,...,im}
V
R ⊆U × I ×V
Iu
f :U × I →V
i* = argmax
j∈I Iu
f (u, j)

Core Recommendation Techniques
¨ U is a set of users
¨ I is a set of items/products
Technique Background Input Process
Collaborative Ratings from U of items in I Ratings from u of items in I Identify users in U similar to u,
and extrapolate from their
ratings of i
Content-based Features of items in I u’s ratings of items in I Generate a classifier that fits
u’s rating behavior and use it
on i
Demographic Demographic information
about U and their ratings of
items in I
Demographic information
about u
Identify users that are
demographically similar to u,
and extrapolate from their
ratings of i
Utility-based Features of items in I A utility function over items
in I that describes u’s
preferences
Apply the function to the items
and determine i’s rank
Knowledge-
based
Features of items in I.
Knowledge of how these items
meet a user’s needs
A description of u’s needs
or interests
Infer a match between i and
u’s need

Group recommendation introduction

Group Recommendation
¨ Designed for contexts in which more than one
person is involved in the recommendation process
I’m a
vegetarian!
I’m on a
diet
I love Asian
food
Where shall we dine?

Application scenarios
¨ Any scenario that involves a decision making process
and a group of users
¤ People dining together (“Where shall we dine?”)
¤ Friends going to the cinema (“Which movie shall we
watch?”)
¤ Groups planning a trip (“Where shall we go?”)
¤ …

Problem statement
¨ We are given:
¤ a set of users
¤ a set of items
¤ a set of values (e.g., V=[1,5] or V={like,dislike})
¨ Let be a ternary relation that contains
the preferences given by the users
U = {u1,u2,...,un}
I = {i1,i2,...,im}
V
R ⊆U × I ×V

Problem statement
¨ Let the set of users U be split into K groups, where
each group respects the following properties:
¤ all the users in gk receive the same recommendations
¤ each user in U has to belong to a group in order to
receive the recommendations:
¤ groups are formed by sets of users who don’t intersect
(each user receives just one set of recommendations):
gk ⊆ U
∀u ∈U ∃ k ∈ {1,...,K} s.t. u ∈ gk
∀k,q ∈ {1,...,K} k ≠ q ⇒ gk ∩gq = ∅

Problem statement
¨ Given a group the objective is to define a
function and to identify an item i* with
the highest predicted rating:
gk ⊆ U
f :gk × I →V
i* = argmax
j∈I
f (gk, j)

Challenges
1. How should the different types of group be handled
in the recommendation process?
2. Should the preferences be collected for each user or
for the group?
3. How should the individual preferences for an item be
merged into a group one?
4. Should the ratings be predicted for each user or for
the group?
5. Who should choose the items to recommend to the
group?
6. How can the recommendations be explained to the
group?

Tasks and state of the art survey

1. Types of group
2. Preference acquisition
3. Group modeling
4. Rating prediction
5. Help the members to achieve consensus
6. Explanation of the recommendations

1. Types of group

Types of group
¨ Different types of groups lead to different ways in
which the preferences can be modeled [Boratto and
Carta 2011][Carvalho et al. 2013]
¨ A group recommender system can work with:
¤ an established group who share the same long-term interests,
like a group of fans of an artist
¤ an occasional group who has a common specific aim, like
visiting a museum
¤ a random group of people who do not have anything in
common (e.g., the recommendation of background music in a
room)

Types of group
Established groups in the literature
¨ PolyLens [O’Connor et al. 2001]
¤ Movie recommendation, considering that people usually
go to the cinema with the same group
¨ GRec_OC (Group Recommender for Online
Communities) [Kim et al. 2010]
¤ Book recommender system for online communities (i.e.,
people with similar interests that share information)

Types of group
Occasional groups in the literature
¨ MusicFX [McCarthy and Anagnost 1998]
¤ Music recommendation to people working out in a gym
at a given time
¨ INTRIGUE [Ardissono et al. 2003]
¤ Suggest tourist attractions to groups of users traveling
together
¤ The system can work with subgroups, to weight
differently people with special needs (e.g., children or
disabled people)

Types of group
Occasional groups in the literature
¨ [Liu et al. 2012] defines event-based social networks,
i.e., communities of people who attend social events,
by considering both online and offline interactions

Types of group
Random groups in the literature
¨ G.A.I.N. [Pizzutilo et al. 2005]
¤ Recommends news to a group of users that are in a
public space at a specific time
¨ FIT (Family Interactive TV System) [Goren-Bar and
Glinansky 2004]
¤ Looks at the probability of each family member to
watch TV in a time slot and predicts who there might be
watching TV

Types of group
Random groups in the literature
¨ Flytrap [Crossen et al. 2002] and Jukola [O’Hara et
al. 2004]
¤ Select music to be played in a public room
¤ Flytrap considers the preferences of the users present in
the room at the moment of the song selection
¤ Jukola allows artists to upload their MP3s and those in
the room can express their vote

2. Preference acquisition

Preference acquisition
¨ A system can acquire explicit or implicit preferences
¨ They can be collected considering that
¤ a user is a part of a group (group preferences),
¤ or not (individual preferences)
¨ Observational studies show that when individual
users interact, their preferences evolve [Delic et al.
2016]
¨ The type of preference acquisition leads to
completely different ways in which information is
handled by the system

Group preferences in the literature
¨ In CATS [McCarthy et al. 2006] members interact and
express their preferences around a shared device called
“DiamondTouch table-top”

¨ In Travel Decision Forum [Jameson 2004] each member of
the group can view and copy the preferences of the other
members

¨ In [Gartrell et al. 2010], the system allows both
individual and groups to express preferences (e.g.,
a couple watching a movie together)
¨ In [Chen et al. 2008] it is assumed that both
individuals and subgroups express preferences

Individual preferences in the literature
¨ CoFeel [Chen and Pu
2013] allows to
express through colors
the emotions given by
a song chosen by the
GroupFun music group
recommender system

Individual preferences in the literature
¨ MusicFX [McCarthy and Anagnost 1998] lets users
express also negative ratings (range [-2,2])
¨ Adaptive Radio [Chao et al. 2005] focuses only on
negative preferences
¤ To avoid playing music that might be disliked by
anyone

Theoretical study
¨ [Xie and Lui 2015] consider the fact that
recommender systems work with partial information
¤ Moreover, some users cheat (misbehavior)
¨ What is the minimum number of ratings a product
needs so that one can make a reliable evaluation of
its quality?
¨ Developed theoretical models, validated on Flixter
and Netflix data in the group recommendation
context

Theoretical study
¨ n’: minimum number of ratings needed to tolerate
the misbehaving users
¨ Pr[n’ ≥ n]: the fraction of movies with a minimum
number of ratings larger than or equal to n

3. Group modeling

Group Modeling
¨ In order to derive a group preference for the items,
group modeling strategies combine the individual
user models
¨ “There is no strategy useful in every context
independently from the environment” [Pizzutilo et al.
2005]
¤ The strategy that best models a group has to be
evaluated in the context in which the group is modeled

Group Modeling
¨ This topic has been mainly studied by J. Masthoff
¤ More than 10 years
¤ Most recent work that involves all the strategies is
[Masthoff 2015]
¨ 11 existing strategies

Group Modeling Strategies Survey

Group Modeling Strategies
¨ When presenting each strategy, we will use the
following example:
¤ 3 users (u1, u2, u3)
¤ 10 items (i1,…,i10)
¤ Each element of the table represents a rating (1,…,10)
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10

1. Additive Utilitarian
¨ Add individual ratings for each item
¨ Also known as Average Strategy
¤ The ordered ranking of the items for a group is the
same
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 20 21 21 25 26 28 22 15 14 23

1. Additive Utilitarian
Uses in the literature
¨ Pocket RestaurantFinder [McCarthy 2002] recommends
restaurants to a group of people, by averaging the
individual preferences of the group members on
different types of features (location, cost, cuisine, …)
¨ In [Amer-Yahia et al. 2009], the modeling strategy
averages the individual preferences also taking into
account the disagreement of the group members for an
item
¨ [De Pessemier et al. 2013] illustrate that modeling users
with an average is the best way to model individual
preferences in different contexts

2. Multiplicative Utilitarian
¨ Multiplicate individual ratings for each item
¨ [Masthoff 2011] showed it is the strategy that
works best when selecting a sequence of television
items to suit a group of viewers
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 280 100 336 540 648 800 270 120 84 420

3. Borda Count
¨ Each item gets a number of points, according to the
position in the list of each user
¤ Least favorite item è 0 points
¤ A point is added for the following item
¤ Same rating to more items è points are distributed
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10

3. Borda Count
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
i8 and i9 è Least favorite items for u2
Share the lowest points: (0+1)/2=0.5

3. Borda Count
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 4.5 8 3 8 6 4.5 8 1.5 0 1.5
u2 3.5 7.5 2 6.5 5 7.5 6.5 0.5 0.5 3.5
u3 2.5 0 5 3 6 7.5 1 2.5 4 7.5
Group 10.5 15.5 10 17 17 19.5 15.5 4.5 4.5 12.5
i8 and i9 è Least favorite items for u2
Share the lowest points: (0+1)/2=0.5

3. Borda Count
¨ [Masthoff 2011] showed it is one of the strategies
that generates most satisfaction when selecting a
sequence of television items to suit a group of
viewers
¨ TravelWithFriends [De Pessemier et al. 2015] uses it
to rank the top-5 travel destinations to recommend
to a group

4. Copeland Rule
¨ Form of majority voting
¨ Sort the items according to their Copeland index
¤ number of times in which an alternative beats the
others, minus the number of times it loses
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10

4. Copeland Rule
¨ Form of majority voting
¨ Sort the items according to their Copeland index
¤ number of times in which an alternative beats the
others, minus the number of times it loses
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Item i2 beats item i1, since both u1 and u2
gave a higher rating to it

4. Copeland Rule
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
i1 0 + - + + + + - - 0
i2 - 0 - 0 - 0 0 - - -
i3 + + 0 + + + + - - +
i4 - 0 - 0 - + - - - -
i5 - + - + 0 + + - - -
i6 - 0 - - - 0 - - - -
i7 - 0 - + - + 0 - - -
i8 + + + + + + + 0 0 +
i9 + + + + + + + 0 0 +
i10 0 + + + + + + - - 0
Index -2 +6 -3 +6 +1 +8 +4 -8 -8 -2

4. Copeland Rule
¨ The approach proposed in [Felfernig et al. 2012]
proved that a form of majority voting is the most
successful in a requirements negotiation context

5. Plurality Voting
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
¨ Each user votes for her/his favorite option
¨ If more than one alternative needs to be selected,
the items that received the highest number of votes
are selected

5. Plurality Voting
are selected
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
User u1 selects items i2, i4, i7

5. Plurality Voting
1 2 3 4 5 6
u1 i2, i4, i7 i4, i7 i5 i1 i3 i8
u2 i2, i6 i4, i7 i5 i1 i3 i8, i9
u3 i6, i10 i10 i10 i10 i3 i9
Group i2, i6 i4, i7 i5 i1 i3 i8, i9
User u1 selects items i2, i4, i7
are selected

5. Plurality Voting
¨ This strategy was implemented and tested by [Senot
et al. 2010] in the TV domain

6. Approval Voting
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
¨ A point is assigned to all the items a user likes
¤ Suppose that each user votes for all the items with a
rating above a certain threshold (let’s say 5)

6. Approval Voting
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10

6. Approval Voting
¨ Group rating for an item: sum of the individual votes
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 1 1 1 1 1 1 1 1 1
u2 1 1 1 1 1 1 1 1
u3 1 1 1 1 1 1
Group 2 2 3 3 3 3 2 1 1 3

6. Approval Voting
¨ To choose the Web pages to recommend to a
group, Let’s Browse [Lieberman et al. 1999]
evaluates if the page currently considered by the
system matches with the user profile above a
certain threshold and recommends the one with the
highest score
¨ It also proved to be successful in contexts in which
the similarity between the users in a group is high
[Bourke et al. 2011]

7. Least Misery
¨ Group rating: lowest rating expressed for an item
by any of the members of the group
¤ usually adopted to model small groups, to make sure
that every member is satisfied
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 5 1 6 6 8 8 3 4 3 6

7. Least Misery
¨ This strategy is used by PolyLens [O’Connor et al.
2001], in order to produce movie recommendations
that satisfy the small groups handled by the system.
¨ GroupLink [Wei et al. 2016] recommends a set of
activities to a group of users. Each user has to be
recommended a minimum number of activities s/he
enjoys

8. Most Pleasure
¨ Group rating: the highest rating expressed for an
item by a member of the group
¨ This strategy is used by [Quijano-Sanchez et al.
2012] in a system that faces the cold start problem.
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 8 10 8 10 9 10 10 6 7 10

9. Average without Misery
¨ Group rating: average of the ratings assigned by
each user for that item
¨ The items with a rating under a certain threshold
are not considered (in the example, 4)
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10

i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 20 - 21 25 26 28 - 15 - 23
¨ Group rating: average of the ratings assigned by
each user for that item
¨ The items with a rating under a certain threshold
are not considered (in the example, 4)

¨ In order to model the preferences of the group for
each genre of music to play in a gym, MusicFX
[McCarthy and Anagnost 1998] sums the individual
ratings expressed by each user, discarding the ones
under a minimum degree of satisfaction.

10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group
¨ Idea: users can be recommended something they do
not like, as long as they also get recommended
something they like
¨ Each user chooses her/his favorite item
¤ Two items with the same rating è choice is based on
the other users

10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4
something they like
¨ Each user choose her/his favorite item
the other users

10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6
something they like
the other users

10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6 i10
something they like
the other users

10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6 i10 i5
something they like
the other users

10. Fairness
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group i4 i6 i10 i5 i2 i7 i1 i3 i9 i8
something they like
the other users

10. Fairness
¨ This strategy is adopted by [Christensen and
Schiaffino 2011] in the music recommendation
context

11. Most Respected Person (Dictatorship)
¨ Select the items according to the preferences of the
most respected person
¤ Using the preferences of the others just in case more
than one item received the same evaluation
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
u1 8 10 7 10 9 8 10 6 3 6
u2 7 10 6 9 8 10 9 4 4 7
u3 5 1 8 6 9 10 3 5 7 10
Group 8 10 7 10 9 8 10 6 3 6
In the example, the most respected person is
u1

11. Most Respected Person (Dictatorship)
¨ This strategy is used by INTRIGUE [Ardissono et al. 2003]
that advantages the preferences of a subset of users with
particular needs
¨ G.A.I.N. [Pizzutilo et al. 2005] shows that when people
interact, a user or a small portion of the group influences the
choices of the whole group
¨ In [Jung 2012], long tail users are considered, i.e., an expert
group on a certain attribute. Their ratings are considered to
provide recommendations to the non-expert user group
(short head group)
¨ When the group model of a family is built in [Berkovsky and
Freyne 2010], the person who prepares the recipe has a
higher weight w.r.t. to the partner and the children

4. Rating prediction

Rating prediction
¨ Ratings can be predicted using one of the following
3 approaches [Jameson and Smyth 2007]:
1. based on a group model: combine individual
preferences and use it to build predictions for the
group
2. merging recommendations built for the users in a
group
3. aggregating all the predictions built for the users in a
group

Rating prediction
Construction of group preference models
¨ Build a group model to combine individual
preferences, then predict a rating for the items that
do not have a score in the group model
¨ Two main steps:
1. Construct a model Mg for a group g (it contains its
preferences)
2. For each item i not rated by the group g, use Mg to
predict a rating pgi

Rating prediction
¨ MusicFX [McCarthy and Anagnost 1998] decides the
genre of music to play by randomly selecting one of the
top-m stations available in the group model that
summed the individual preferences
¤ Random to avoid playing the top genre everyday
n The same people might work out at the same time and the same
genre would be played everyday
¨ INTRIGUE [Ardissono et al. 2003] models the
preferences of subgroups of homogeneous people, then
produces the recommendations giving a different
importance to particular categories of people (e.g.,
disabled people)

Rating prediction
¨ [Berkovsky and Freyne 2010] showed that when
recommending recipes to a family, a group model
that combines the individual preferences should be
used to make the predictions
¨ To recommend TV programs, TV4M [Yu et al. 2006]
builds a model with the family members who
logged in (i.e., who are in front of the TV)

Rating prediction
Merging individual recommendations
¨ Present to a group a set of items, i.e., the merging
of the items with the highest predicted ratings for
each user in the group
¨ The approach works as follows:
1. For each user u in the group:
n For each item i not rated, predict a rating pui
n Select the set Cu of items with the highest predicted
ratings pui
2. Model the preferences of each group by producing
U Cu

Rating prediction
Merging individual recommendations
¨ The approach is not widely used in the literature
¨ PolyLens [O’Connor et al. 2001] selects the items
with the highest predicted ratings for each user
¤ Then employs a Least Misery strategy to recommend
the ones with the lowest rating

Rating prediction
Merging individual predictions
¨ Predict individual preferences for all the items not
rated by each user, then aggregate individual
preferences for an item into a group model
¨ The approach works as follows:
1. For each item i:
n For each user u who did not rate i, predict a rating pui
n Calculate an aggregate rating rgi from the ratings of the
users in the group

Rating prediction
¨ Pocket RestaurantFinder [McCarthy 2002] predicts
a rating for each user and each restaurant and
combines them with an average
¨ Travel Decision Forum [Jameson 2004] builds
predictions for every user (users can copy the
preferences for the others), than predicts a group
score by considering the median of the individual
predictions

Rating prediction
¨ E-Tourism [Garcia et al. 2009, Sebastia et al.
2009] build three types of predictions for each
user (demographic, content- and like-based),
aggregates them and selects the group
recommendations from each list

5. Help the members to achieve consensus

Help the members to achieve a consensus
¨ Three strategies are usually employed to select the
items to recommend to the group:
1. the system suggests the items with the highest
predicted ratings, without consulting the group;
2. a member of the group is responsible for the final
decision;
3. the users in the group have a conversation, in order to
achieve consensus.

Member responsible for the final decision
¨ Travel Decision Forum [Jameson 2004] allows the
tourist guide to make the final decision
¨ In [Ben-Arieh and Chen 2006], an expert in the
group expresses opinions on an alternative through
linguistic labels (e.g., perfect) and the system
aggregates these labels to make a decision

Conversation between the users
¨ Travel Decision Forum [Jameson 2004] also allows
users to have a conversation
¨ If they’re not in the same room, animated characters
(agents) represent the likely response of the abstent
users

6. Explanation of the recommendations

Explanation of the recommendations
¨ The systems deal with preferences of multiple users
¨ Some explain why the proposed items have been
selected for the group

¨ PolyLens [O’Connor et al. 2001] presents the group
recommendations by showing also the individual
ones

¨ Let’s Browse
[Lieberman et
al. 1999] shows
the keywords
that led to the
recommendation

¨ INTRIGUE [Ardissono et al. 2003] gives a long
explanation of why a destination was recommended
to a group

Evaluation methods
¨ Three approaches:
1. Offline methods on existing datasets
2. User surveys that that test the effectiveness of a
system by asking users to answer questionnaires
3. Live systems that work in real-world domains, like the
social networks

Evaluation methods
Offline methods
¨ Employ classic evaluation metrics:
¤ RMSE
¤ MAE
¤ Precision and Recall
¤ …

Evaluation methods
Offline methods
¨ No public group recommendation dataset is
available in the literature [Padmanabhan et al.
2011, Quijano-Sanchez et al. 2012]
¤ The partitioning of the users into groups is not available
¨ The vast majority of the approaches adds
constraints on a dataset to infer the groups and
build the recommendations

Evaluation methods
User surveys
¨ Users are asked to compile questionnaire to
evaluate the system from several perspectives:
¤ The quality of the recommendations [De Pessemier et
al. 2016]
¤ The usability of the system [Zapata et al. 2015]

Evaluation methods
Live systems
¨ GroupLink [Wei et al. 2016] suggests events to
promote group members’ face-to-face interactions in
non-work settings
¨ Identifies and tracking personal preferences by
analyzing individual digital traces (social media,
email, and online streaming histories)
¨ A live system has been developed:
https://bit.ly/group-link

Emerging aspects and techniques

1. Advanced recommendation techniques applied to
group recommendation
2. Social group recommender systems
3. Fairness in group recommendations

Advanced recommendation techniques

¨ Over the last few years, new recommendation
techniques have been developed to address problems
such as:
¤ sparsity
¤ limited coverage
¨ Two main research directions:
¤ dimensionality reduction
n Compact representation of users and items (most significant
features)
¤ graph-based techniques
n Exploit the transitive relations in the data
¨ They have been recently adopted in group
recommendation problems

Dimensionality reduction
¨ [Christensen and Schiaffino 2013] employ matrix
factorization and SNA (to analyze social influence)

Graph-based techniques
¨ [Kim and El Saddik 2015] present a stochastic method
¤ Build a bipartite graph and perform random walks to
quantify the influence of nodes (i.e., users and items) and
rank items to recommend to groups

Graph-based techniques
¨ COM (COnsensus Model) [Yuan et al. 2014] builds a
generative model that incorporates users’ selection
history and personal considerations of content factors
¨ Users in a group may have different influences (e.g.,
expert in a topic)

Social group recommender systems

Social group recommender systems
¨ HappyMovie [Quijano
Sanchez et al. 2014] is
a Facebook application
that recommends
movies to groups
¨ It considers user
preferences, social
interactions, personality
of the users, …
¨ 60 users (35 males and
25 females) tested and
evaluated the
application

Fairness in group recommendation

¨ User groups may be heterogeneous, consisting of
people with potentially dissimilar preferences.
¨ If an item is overall good for the group, there could
be one or more members that do not like it
¨ These users would be frustrated if the item is
selected by the group!
¨ Measuring how fair are the items recommended for
a group is central

¨ [Qi et al. 2016] and [Serbos et al. 2017] study
fairness in the package-to-group
recommendation scenario. The two works
introduce two metrics:
1. m-Proportionality: For a user u, and a package P, P
is m-proportional for u, for m ≥ 1, if there exist at
least m items in P that u likes. For a group of users G,
and a package P, the m-proportionality of the
package P for the group G is defined as: |GP|/|G|
n where GP ⊆ G is the set of users in the group for which
the package P is m-proportional.

2. m-Envy-Freeness: a user u feels that a package is
fair, if there are m items for which the user is in the
favored top-∆% of the group. Otherwise, the user has
envy against the other members of the group, who
always get a better deal, and thus feels she is being
treated unfairly. For a group of users G, and a
package P, the m-envy-freeness of the package P for
the group G is defined as: |Gef|/|G|
n where Gef ⊆ G is the set of users in the group for which
the package P is m-envy-free.

¨ [Lin et al. 2017] recommend items to a group, by
ensuring fairness thanks to Pareto efficiency
¨ A solution is called Pareto efficient if none of the
objective functions can be improved without degrading
some of the other objectives.
¨ Several greedy algorithms that optimize different
fairness metrics are proposed and the most effective is
that based on the variance of the ratings of the users:
FVar(g,I) = 1-Var({U(u,I), ∀u∈g}
¨ This last solution outperform the two previous metrics in
terms of accuracy

Group recommendation with automatic detection
of groups
Case Study

Group recommendation with automatic
detection of groups
¨ Example:
recommendation flyers
¨ Nielsen estimates that
1B Euros per year is
spent to print 12M
flyers
¨ 14.6B Euros are
estimated to be spent
by the customers thanks
to these flyers
http://www.nielsen.com/content/dam/c
orporate/Italy/reports/2012/Le nuove
tendenze del largo consumo (R. de
Camillis).pdf

Group recommendation with automatic
detection of groups

Group recommendation and automatic
detection of groups
¨ Research questions:
1. How should we predict the ratings in this context?
n individual predictions for each user?
n group predictions?
2. How should we group the users for recommendation
purposes?
3. How should we generate group models that contain
the preferences for a group?

Group recommendation and automatic
detection of groups
¨ [Boratto and Carta 2015] shows that:
1. Ratings should be predicted for individual users
2. Groups should be detected with a clustering
algorithm (k-means) that also includes the predictions
in the input
3. Groups should be modeled through an average of
the individual ratings (Additive Utilitarian)
n It represents the centroid of the cluster

Open issues and research
challenges

Open issues and research challenges
¨ No public dataset available
¤ With both group structure and individual/group preferences
¨ Evaluation
¤ How effective are the group recommendations? Consider both
individual satisfaction and that of the group as a whole
¨ Explanations with model-based algorithms
¤ Recommendations are based on latent features and explaining
them is challenging
¨ Understanding and employing group dynamics
¤ Integrating the evolution of the individual preferences that
happens because of the group dynamics is still an open issue
¨ Novelty, diversity, and serendipity
¤ Generating novel, diverse, and serendipitous recommendations
for the whole group is challenging

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