Social Utilities and Personality Traits for Group Recommendation: A

Pilot User Study

Silvia Rossi and Francesco Cervone

Dipartimento di Ingegneria Elettrica e Tecnologie dell’Informazione,

Universita’ degli Studi di Napoli “Federico II”, Napoli, Italy

Keywords:

Social Choice, Group Recommendation, Big-Five, Group Decision Making.

Abstract:

Recommendations to a group of users can be provided by the aggregation of individual users’ recommenda-

tions using social choice functions. Standard aggregation techniques do not consider the possibility of evalu-

ating social interactions, roles, and inﬂuences among group’s members, as well as their personalities, which

are, indeed, crucial factors in the group’s decision-making process. Instead of deﬁning a speciﬁc social choice

function to take into account such features, the proposed solution relies on the deﬁnition of a utility function,

for each agent, that takes into account other group members’ preferences. Such function models the level of

a user’s altruistic behavior starting from his/her agreeableness personality trait. Once such utility values are

evaluated, the goal is to recommend items that maximize the social welfare. Performance is evaluated with a

pilot user study and compared with respect to Least Misery. Results showed that while for small groups LM

performs slightly better, in the other cases the two methods are comparable.

1 INTRODUCTION

Recommender Systems are powerful tools that pro-

vide suggestions about items for users. The list of

recommended items is the result of a decision process

that aims to understand, for example, which product

a speciﬁc user wants to buy, which music to listen,

which movie to see or which news to read. In this

context, the goal of Group Recommender Systems

(GRSs), differently from Individual Recommender

Systems (or simply RSs), is to recommend items to

a whole group of users taking into account all the in-

dividual preferences. The study of GRSs is still an

open research problem. Preferences and tastes of in-

dividuals are collected in the same way of RSs, but the

real problem falls into the correct aggregation of these

preferences in order to recommend items that maxi-

mize global satisfaction, or at least, minimize dissat-

isfaction of all the members.

The problem of aggregating individual prefer-

ences has been widely studied in Mathematics, Eco-

nomics and Multi-agent systems (MAS), with the def-

inition of Social Choice functions. In literature, sev-

eral solutions have been proposed (Masthoff, 2011).

The most common technique is the Average Satis-

faction (AS): it treats all group members as being

equal by averaging preferences (or recommendations)

of all the member in order to produce a ﬁnal list for

the entire group. Least Misery (LM), instead, cares

about the possible dissatisfaction of some members

by choosing items that minimize it. Anyway, no one

of the standard techniques considers that there are

also other factors that can inﬂuence the group deci-

sion. Groups can be dynamic, as well as members’

behaviors depending on situations. Real group deci-

sion making is a complex mechanism that involves re-

lationships among the members, users’ personality, as

well as their experience about the domain of interest.

In this work, we study the inﬂuence of individ-

ual users personality in the group decision-making

process. In a realistic scenario, indeed, the person-

alities of the group members can have an impact on

group decisions. For example, there can be people

that rarely change their minds because they believe

that their own decision is the best for everyone, or

simply because they do not want to reduce their util-

ity in favor of others. Other types of people instead,

can be worried about the satisfaction of all the other

members, at the cost of the personal one. Thus, the

latter ones are willing to lose some utility in order

to reach a valid and suitable agreement for the entire

group. In order to consider these elements, it is neces-

sary to study users’ personalities through some mod-

els proposed in human sciences area. One of the most

common is certainly the Five-Factor Model (FFM).

Rossi, S. and Cervone, F.

Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study.

DOI: 10.5220/0005709600380046

In Proceedings of the 8th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2016) - Volume 1, pages 38-46

ISBN: 978-989-758-172-4

According to this, the behavioral features of a person

can be summarized and described through ﬁve fac-

tors, called also Big-Five. They are: openness, con-

scientiousness, extraversion, neuroticism and agree-

ableness (Costa and MacCrae, 1992). In particular, in

this work, we focus on the role of the agreeableness

factor in the deﬁnition of a utility function that mod-

els altruistic behavior.

In literature, some approaches are starting to

model GRSs that weights users preferences in dif-

ferent ways according to a speciﬁc user-related pa-

rameters. For example, in Gartrell et al. (2010) users

preferences are weighted according to their expertise,

while in Rossi et al. (2015) users’ dominance and in-

ﬂuence are taken into consideration. In the proposed

approach, we do not consider weights in the aggre-

gation process, but the utility function used to eval-

uate users rating on individual items takes into ac-

count the whole group preferences and the user agree-

ableness trait. In this sense, our work is related to

the approach of Salehi-Abari and Boutilier (2014),

where individual empathetic utilities are deﬁned tak-

ing into account local relationships with neighbor-

hoods in a social network. However, in Salehi-Abari

and Boutilier (2014) the Authors do not specify how

to evaluate such numerical relationships, while they

focus on computational aspects of scaling up with

large networks of friends. While the role of person-

ality has been addressed before, in literature, to im-

prove the performance of RSs (Nunes and Hu, 2012;

Hu and Pu, 2011), up to our knowledge, this is the

ﬁrst attempt to introduce personality factors in group

decision making through the use of personality-based

utility functions. The only relevant approach is the

one of Quijano-Sanchez et al. (2013), where the per-

sonality of every individual in the group is evaluated

in terms of conﬂicting resolution styles. The user’s

ranking of an item is modiﬁed by considering all the

couples of users and their mutual inﬂuence.

This paper is organized as follows. In Section 2,

we introduce the Five-Factor Model used to evaluate

users’ agreeableness personality trait values, while,

in Section 3, we introduce the utility function used

to model altruistic behaviors and the evaluation, by

personality tests, of parameters that characterize such

function. In Section 4, we present the developed ap-

plication and the metrics used to evaluate with real

users the proposed utility function with respect to

Least Misery (LM). Finally, results are discussed in

Section 5.

2 BIG-FIVES AND

PERSONALITY TRAITS

Research has shown that personality is a primary fac-

tor which inﬂuences human behaviors. The Five-

Factor Model (FFM) describes human personality

using ﬁve factors, also known as Big-Five (McCrae

and Costa, 1987). Neuroticism represents an emo-

tional instability characterized by negative emotions

like fear, anger, sadness and low self-esteem (Mc-

Crae and Costa, 1989). High neuroticism level peo-

ple rarely control their impulses and cope the stress

(McCrae and Costa, 1985). Extraversion is an indica-

tor of assertiveness and trust. Extravert people easily

create interpersonal relationships and love working

and being together with others (Costa and McCrae,

1995). Agreeableness describes the level of sympa-

thy, availability and cooperativeness. People with low

level of this factor are competitive, skeptics and an-

tagonistic. It measures how much a person is nice and

altruist (Costa and McCrae, 1995). Openness repre-

sents the inclination to openness to new experiences,

having an active imagination and a preference about

the will to ﬁnd new ideas (Costa and McCrae, 1995).

Closed people are less ﬂexible and rarely understand

others’ point of views. Finally, conscientiousness de-

scribes how much an individual is responsible, disci-

plined and dutiful (McCrae and Costa, 1989).

Several studies also demonstrated that personality

traits have an impact in group decision making. An-

derson et al. (2001) tried to correlate Big-Five with

the status

of college students. A student with a high

level of status implies a considerable level of atten-

tion by others towards him. Furthermore, status re-

lates to respect and inﬂuence in the social context.

Authors measured students’ status through an assess-

ment made by the student themselves and according

to the positions that they play in the college organiza-

tion. Results showed that the extraversion has a pos-

itive correlation with the status; conversely, neuroti-

cism has a negative correlation, but only for men. The

latter result can be explained because Brody (2000)

said that sadness, depression, fear, shame and embar-

rassment (neuroticism features) are viewed as “un-

manly”. Thus, men who show these emotions are

evaluated more negatively with respect to women. For

the latter, these emotions are considered ordinary.

Sager and Gastil (2006) studied the impact of Big-

Five in producing a supportive communication. The

latter is opposed to defensive communication in which

group members see others as a threat and try to stay

one step ahead them. In supportive communication

Status describes the role that a person has in a social

group.

Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study

instead, all the members allow others to express their

own opinions and consider other choices. Extraver-

sion, agreeableness and openness has a positive cor-

relation in this context.

Ma (2005) studied the inﬂuence of personality in

conﬂict situations that happen during negotiations.

He correlates conﬂict resolution styles, which rep-

resent different people reactions in case of conﬂict,

with big ﬁve by using two factors: the assertiveness,

which describes how much an individual try to satisfy

his needs, and cooperativeness, which suggests the

level of collaboration and the intention of maximizing

others’ utilities. Results showed that: neuroticism is

negatively correlated to compromising; extraversion

is positively correlated to competing and collaborat-

ing; agreeableness is negatively correlated to compet-

ing and positively to compromising; ﬁnally, consci-

entiousness and openness have no signiﬁcant correla-

tions.

Starting from the previous considerations, in this

work, we decided to move a ﬁrst step by considering

only a single personality trait at the time starting from

the agreeableness trait, while leaving the combination

with the others as a future work. We decided not to

rely on the evaluation of neuroticism, which has neg-

ative correlations with status (a neurotic person could

be excluded during a decision process). Conversely,

extraverts could be viewed as leaders, but at the same

time they are more competitive than collaborative: all

of those are qualities that are suitable to our case (the

choice of a movie), but already considered in related

works. A conscientious person could have a positive

impact when deciding important decision, but we do

not believe it is signiﬁcant for our speciﬁc goal. This

is the same for open individuals which could agree to

see a movie not suitable with respect to their prefer-

ences, but whose trait could be more difﬁcult to model

with respect to the agreeableness factor.

We believe that in choosing a movie in a group of

close friends, agreeableness (that is related to altruis-

tic behavior (Costa and McCrae, 1995)) plays an im-

portant role. It is obvious that, unless all the compo-

nents have the same movie tastes, someone will have

to give up their desires to see a movie which others do

not like. Therefore, agreeable people will make com-

promises and, for this reason, we decided to analyze

the impact of this factor in a GRS. People with high a

value of agreeableness, just because of its descriptive

characteristic, are more altruistic: basically, it means

that this type of people care about the satisfaction of

the entire group, or are more willing to compromise

in order to obtain a solution that works for the whole

group.

3 AN UTILITY FUNCTION FOR

AGREEABLENESS

Generally speaking, the aim of a Recommendation

System (RS) is to predict the relevance and the im-

portance of items (for example movies, restaurants

and so on) that the user never evaluated. More for-

mally, given a set of n users (U = {1, . . . , n}) and a set

of m items (M = {1, . . . , m}), in our domain applica-

tion it is a set of movie, an individual recommendation

system, for each user i, aims at building a Preference

Proﬁle of the user i over the complete set M, start-

ing form some initial ratings (typically a value from

1 to 5) each user provides on some elements of M.

Once each user i ∈ U has a preference proﬁle 

over

M (

= {x

i,1

, . . . , x

i,m

}) with x

i, j

∈ R , which repre-

sents the user i rank (as directly expressed by the user

i or evaluated by the system) for the j movie, the goal

of a GRS is to obtain 

= {x

U,1

, . . . , x

U,m

}, where

U, j

is the correspondent ranking for the j movie, as

evaluated for the group. Typically, this is obtained by

implementing a social choice function SC :

→

, that aggregates all the preferences proﬁles in 

U,1

, . . . , x

U,m

}. Note that our goal is not to guess the

exact value of x

U, j

the whole group would assign to

the movie j, but to properly select the k-best movie in

the group preference proﬁle (the ones with the highest

rating) and suggest them to the group.

In this work, we propose to consider the agree-

ableness factor in the process of building recommen-

dations for groups. Instead of deﬁning a speciﬁc so-

cial choice function that considers such factor, the

proposed solution relies on the deﬁnition of an in-

dividual utility function (to build up the preference

proﬁle) to evaluate the rating of items for each user,

that takes into account the whole group. Such utility

function could be interpreted as “the user satisfaction

if the recommender system chooses that item for the

group”. For the utility function, we used a model de-

veloped by Charness and Rabin (2002).

Let us consider a group of n users, which have a

rating for each element j of the domain (e.g., an ex-

pected rating provided by an individual RS x

i j

). The

utility function is composed by two terms; the ﬁrst

concerning a “disinterested” social-welfare criterion,

deﬁned as follows:

W (x

1, j

, x

2, j

, ..., x

n, j

) = δ · min(x

1, j

, x

2, j

, ..., x

n, j

(1 − δ) · (x

1, j

+ x

2, j

+ ... + x

n, j

)

(1)

where δ ∈ [0, 1], x

1, j

, x

2, j

, ..., x

n, j

are called payoffs

and they are the individual rating of each of the n

users of the j-th item. The aim of the ﬁrst addend is

reducing inequity (by helping the worst-off person);

ICAART 2016 - 8th International Conference on Agents and Artiﬁcial Intelligence

indeed, this value increases proportionally to the min-

imum payoff. This factor is, basically, a generaliza-

tion of LM technique just because utility is given by

the minimum satisfaction among the users. The sec-

ond addend is responsible for the maximization of the

social welfare and increases proportionally to the sum

of the individual payoffs. Setting δ = 1, indeed, the

function cares only about inequity, just like LM. Set-

ting δ = 0 instead, the function focuses on global sat-

isfaction.

The utility of user i about the item j is a weighted

sum of the disinterested social-welfare W and its own

payoff deﬁned as follows:

i, j

1, j

, x

2, j

, ..., x

n, j

) = (1 − λ

) · x

i, j

·W (x

1, j

, x

2, j

, ..., x

n, j

)

(2)

where λ

∈ [0, 1] means how much a person pursues its

own interest or the social welfare. Setting λ

= 0 user

takes care only of his personal interest. λ

= 1 repre-

sents the classical disinterested behavior, of him/her,

who does not take part in the group decision, mov-

ing the weight of the function on the entire group

satisfaction (including its own). Hence, according to

Charness and Rabin (2002), the considered function

evaluates both the personal and the group satisfaction,

depending on an altruistic factor λ

. Once the new

items utilities are evaluated, the goal is to recommend

movies that maximize the social welfare.

Since the agreeableness factor is positively cor-

related with an altruistic behavior, here, our goal is

to calibrate the λ

parameter with respect to the indi-

vidual agreeableness levels, so that the most altruistic

have a high value and viceversa.

3.1 Personality Test

Personality can be acquired in both explicit and im-

plicit ways (Dunn et al., 2009). The former measures

a user’s personality by asking the user to answer a list

of designed personality questions. These personality

evaluation questionnaires have been well established

in the psychology ﬁeld (Gosling et al., 2003). The

implicit approaches acquire user information by ob-

serving users’ behavioral patterns. Typically, explicit

personality acquisition interface are preferred (Dunn

et al., 2009) by the user. However, implicit meth-

ods require less effort from users. In our study, we

adopted the explicit way to measure users’ personal-

ity, because the evaluation of a single personality trait

would require using only a small set of questions, and

so a minimum required effort from the user.

There are several questionnaires to predict a per-

son’s Big-Five factors. Some of these consist of a

lot of questions, in certain cases some hundreds. Too

Table 1: Mini IPIP questionnaire for agreeableness evalua-

tion.

# Text

1 Sympathize with others’ feelings.

2 Am not interested in other people’s problems. (R)

3 Feel others’ emotions.

4 Am not really interested in others. (R)

(R) = Reverse Scored Item.

long questionnaires can reduce the people’s attention

level. A lot of research groups tried to cut the number

of questions preserving the accuracy. The most fa-

mous small questionnaires are NEO-FFI (NEO Five

Factor Inventory) composed by 60 questions (Costa

and MacCrae, 1992), the 50-item IPIP-FFM (Inter-

national Personality Item Pool - Five Factor Model)

(Goldberg, 1992), the 44-item BFI (Big Five In-

ventory), the TIPI (Ten-Item Personality Inventory)

(Gosling et al., 2003) and, ﬁnally, the Mini-IPIP

(Mini International Personality Item Pool) (Donnel-

lan et al., 2006). We chose the last because it is very

short, but at the same time effective. It consists of

20 questions, 4 for each personality factor. Since our

focus only on the evaluation of the user agreeable-

ness, we extracted the 4 related questions (see Table

1; questions should be read in ﬁrst person). The an-

swer of each question can be a number from 1 to 5,

where 1 means “very inaccurate” and 5 “very accu-

rate”.

3.2 Agreeableness and Group

Recommendation

Let us assume that each user completed the agree-

ableness evaluation test. Generic user i has a personal

value α

∈ [1, 5], obtained by averaging individual an-

swer values. Since the λ

parameter of Equation 2

should belong to the range [0, 1] and should depend

directly from α

, we deﬁned it as follows:

− 1

. (3)

This way, when α

= 5 (high level of agreeable-

ness), λ

= 1, so the user cares only about the group

satisfaction. When α

= 1, instead, λ

= 0, conse-

quently, user’s utility depends only on his personal

satisfaction. As we already explained, in the Equa-

tion 1 there is the δ parameter that deals with weight-

ing inequity aversion against global satisfaction. We

set it equal to 0.5 for giving the same importance to

both goals. Utility function 2 refers to the utility of a

particular item for a speciﬁc user. Thus, preferences

1, j

, x

2, j

, ..., x

n, j

stand for the rating predictions of a

generic item for the n members of the group.

Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study

To build recommendations for the groups, we

chose the merging recommendations technique.

Firstly, the system creates a list of 10 items L

for each

user i evaluated by an RS. Later, it merges all the lists

in a single one which we call L:

L =

[

i∈U

(4)

where, G is the set of members of the group. Our GRS

computes for each user the rating predictions of all the

items in L. The latter are exactly the x

1, j

, x

2, j

, ..., x

n, j

parameters of Equation 2 (where n = |U | and j is the

movie). U

i, j

for each user i and for each item j is the

computed. x

U, j

denotes the utility of the group if the

GRS chooses item j deﬁned as follows:

U, j

∑

i∈U

i, j

. (5)

Our goal is to maximize the social welfare, in-

deed, system will recommend the 10 items with the

highest x

U, j

value.

4 EXPERIMENTAL STUDY

To conduct our experiments, we developed a client-

server application. Client was an Android

app and

the server was developed in Java

using Spring frame-

work

hosted by Tomcat Servlet Engine

4.1 Movie Recommendation Server

We built a RESTful web service JSON-based in

order to communicate with the Android app. We

adopted the Apache Mahout library

to predict

the user ratings, and chosen the MovieTweetings

(Dooms et al., 2013) dataset to train the system

and to populate the Ratings Repository. Movi-

eTweetings consists of movie ratings contained in

well-structured tweets on the Twitter.com social

network. This information is contained in three

ﬁles: users.dat, ratings.dat and movies.dat, which

provide, respectively, the user identiﬁcation number,

his/her associated ratings and a list of movies. The

dataset is updated every day, therefore, its size is

constantly changing. At the last access, it contained

about 35000 users, 360000 ratings and 20000 movies.

https://www.android.com

https://www.java.com/

https://spring.io

http://tomcat.apache.org

http://mahout.apache.org/

The recommendation engine provides rating pre-

dictions when the recommendation API is invoked.

To achieve this goal, we used item-based City Block

distance, also known as Manhattan distance. In Ma-

hout implementation, the generic movie j is repre-

sented by a boolean vector:

j = [x

1, j

, x

2, j

, ..., x

k, j

], (6)

where k is the number of users in the dataset and

i, j

= 1 if user i rated the movie j. The distance be-

tween two movies rated by user i is the sum of the

absolute value of the differences of the two associ-

ated vector components. More formally, the distance

between items j and h is:

d( j, h) =

∑

i=1

i, j

− x

i,h

|. (7)

4.2 Android Application

An Android application was developed in order to

gather the information needed by the server to provide

recommendations to single users. In order to simplify

the operations, the experiment consists in some se-

quential steps so that each phase unlocks the next one.

The ﬁrst duty for the user, when he/she accesses

the application, is to sign up/sign into the system.

The user signs up to the system by entering username,

password, gender, age, and education level. When the

interaction starts, users have ﬁrst to provide a certain

number of movie ratings (at least for 20 movies), with

a value in the range [1, 10] (see Figure 1-left) in order

to deﬁne their proﬁle. The user is provided with an in-

terface to get movie lists and to store movies ratings.

If a user is in the training stage, he/she can browse

movies by ordering them by most rated or best rated,

or searching for a speciﬁc movie (ﬁltering by genre or

title).

Once the user rated twenty movies, the app au-

tomatically shows the personality questionnaire (see

Figure 1-right). It consists of four questions, as previ-

ously explained.

After this ﬁrst stage, a user can get movie rec-

ommendations from the server. When the server

gets the recommendation request, once calculated the

best movies for the user, it retrieves additional de-

tails about the ﬁlm, like, for example, the director,

writers, actors and genres using OMDb

web service.

Fortunately, MovieTweetings data set stores, for each

movie, its IMDb id, which can be used to address the

OMDb service. The Android application shows on

the screen the recommendations for the user through

textual and graphical descriptions.

http://www.omdbapi.com - The Open Movie Database

is a free web service to obtain movie information.

ICAART 2016 - 8th International Conference on Agents and Artiﬁcial Intelligence

Figure 1: Movie rating interface (left) and ﬁrst question of

personality test (right).

4.3 Methodology

The design of this study is a within-subjects, coun-

terbalanced, repeated measures experiment. The goal

of our study is to compare the proposed technique,

described in the previous paragraphs, with respect

to the Least Misery. We selected the latter because

it achieves good performance especially for small

groups (O’connor et al., 2001).

Once the questionnaire is completed, a user can

start the test by completing the following steps.

Firstly, a user creates a group giving it a name, and

adds in it one or more members using their user-

names. The system, then, recommends a list of 10

movies (see Figure 2-left). In order to generate the

list of 10 items for the group, for each technique (the

used utility function and LM) the GRS recommends

10 movies ordered in terms of their ratings. To merge

them in one list of ten items, we developed an iter-

ative algorithm that, at each step, adds an item from

each list (starting from the items with the highest rat-

ing) in the output set. If the current item is already

in the set, the algorithm skips to the next one, and so

on. From this list, the group has to collectively choose

three movies that they would like to see together (see

Figure 2-left). Finally, the group has to sort the three

selected movies (see Figure 2-right) in their joint pref-

erence order.

4.4 Evaluation Metrics

Since groups select the best 3 from a 10 movies set, in

order to evaluate and compare our method with LM,

Figure 2: Recommended movies (left) and sorting page

(right).

we considered the following metrics.

precision@3: is the ratio between the number of

movies guessed by the GRS (using a speciﬁc method)

and the sum of the latter and the remaining movies

(only considering the ﬁrst three movies). If G is the

set of the groups that participated to the experiment,

and P

represents, respectively, the 3 movies se-

lected by group g and the 3 movies with the highest

prediction, then:

precision@3 =

|G|

∑

g∈G

∩ P

| + (3 − |I

∩ P

(8)

nDCG@3: evaluates the ranking of predicted

movies with respect to the real ranking j chosen by

groups.

nDCG@3 =

|G|

∑

g∈G

∑

j=1

rel

max(1, log

(9)

where,

rel

(

1 if i

∈ I

0 otherwise

(10)

x success@3 , for each group, is 1 if the algorithm

guessed at least x movies in the 3 selected by the

group. With 1 ≤ x ≤ 3:

x success@3 =

|G|

∑

g∈G

x success@3

(11)

Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study

where,

x success@3

(

1 if |I

∩ P

| ≥ x

0 otherewise

(12)

5 RESULT ANALYSIS

Experiments lasted about two weeks and, as summa-

rized in Table 2, we recruited 68 users (48 groups)

with an average age of 27 years, the most of whom

were students.

Table 2: User stats.

Number of users 68

Average age 26.9

Minimum age 14

Maximum age 55

Males 45

Females 23

Middle school 1

High school 9

Bachelor students - undergraduate 26

Bachelor students - graduate 11

Master students - undergraduate 11

Master students - graduates 10

Group members were directly selected by one of

the users, as described above, and their intersection is

not necessarily empty (e.g., some users joined more

than one group). A single group was allowed to join

the test only for one time. The number of considered

group was 48 with an average number of members

equals to 2.6. In Table 3, we reported the number of

groups considered in the experiments for each group

dimension.

Table 3: Group stats.

# mebmers Amount

2 22

3 23

4 2

5 1

Total 48

When comparing the two techniques, we analyzed

their results separately for the groups of dimension

two and for the groups of more than two members.

The main reason of this choice is that about the half

of the groups were composed by two members (see

Table 3) and it is known that LM shows the best per-

formances in this case.

precision@3. Results of precision@3 are summa-

rized in Figure 3. From the charts, we can see the

better performance of LM on two members groups. It

is not a surprise, because LM excels in cases like this.

ANOVA test conﬁrms that difference between the two

techniques is signiﬁcant (F = 3.076, p-value= 0.09).

Regarding groups with more than two members,

once again LM is better than our technique, but dif-

ference, in this case, is too small to be signiﬁcant for

ANOVA test (F = 0.11, p-value= 0.74).

Figure 3: precision@3.

nDCG@3. As for the previous metrics, LM over-

comes the proposed technique (see Figure 4), but

not enough according ANOVA (F = 1.547, p-value=

0.22 for the two members groups and F = 0.056, p-

value = 0.81 for the others).

Figure 4: ndcg@3.

1 success@3. We recall that 1 success@3 counts

the number of times that the algorithm guessed at

least one movie among the three selected by the

group. LM is always better than our technique

(see Figure 5), but these results provide signiﬁcant

differences only when considering all the 48 groups

together (F = 3.847, p-value = 0.05).

ICAART 2016 - 8th International Conference on Agents and Artiﬁcial Intelligence

Figure 5: 1 success@3.

2 success@3. In this case, our method was more ac-

curate in groups with more than two members (see

Figure 6). Unfortunately ANOVA shows that these

differences are due to chance (F = 0.305, p-value

= 0.58).

Figure 6: 2 success@3.

3 success@3. It is really rare that a technique is

able to guess exactly the three movies chosen by the

group. However, in some cases, it happened. Once a

time LM wins but with no signiﬁcant differences.

5.1 Discussion

Groups recommendation systems lack the appropriate

dataset to be evaluated upon. On the contrary, user

studies have the disadvantages of being expensive to

conduct both in term of recruiting the proper users

and engaging them, when they are volunteers. In this

sense, founding the appropriate number of groups and

with varying dimensions is challenging.

As expected, for two users groups LM is the best

choice. In the other cases, we cannot say the same

thing since the two methods are comparable and show

a similar performance. Pearson correlation, evaluated

on results distributions shows that the two techniques

are not linearly dependent because, in most cases, its

value is near zero. This result means that, in some cir-

cumstances, the proposed utility function shows a bet-

ter performance than LM and in other cases the oppo-

site occurs. Therefore, in certain groups, users do care

to minimize the group unsatisfaction (i.e., the LM

goal) regardless of the agreeableness value. Since we

ﬁxed the δ value to 0.5, with the aim to give the same

weight to least misery and social welfare components

of Equation 1, a better tuning of this parameter would

results in choices that differentiate most from LM.

Moreover, we think these ﬁrst results are effected

by how we conducted the experiments. As already

said, the 10 movies recommended to the groups are

obtained by a sort of union of two lists independently

generated by the two different techniques. Thus, in

this set, there are movies chosen by both LM and the

proposed utility function. Maybe, a between-subject

experiment (by assigning the result of a single tech-

nique to each group) would lead us to different re-

sults. Moreover, the low number of experiments had

a relevant impact in the signiﬁcance analysis of the re-

sults. Finally, since most of the groups was composed

by two or three members, we foresee that, with larger

groups, our technique could have obtained better re-

sults.

6 CONCLUSIONS AND FUTURE

WORK

In this work, we introduced a new method to predict

which items are suitable for groups of users, taking

into account users’ personality. In particular, we eval-

uated the role of the agreeableness factor (e.g., one

of the features of FFM), in order to weigh the impor-

tance of user’s gain with respect to the global satisfac-

tion.

To evaluate this approach, we conducted a pi-

lot user study on movie recommendations, where we

compare the results of the proposed approach with re-

spect to a Least Misery strategy (LM). Results showed

that for small groups a LM performs slightly better.

In particular, for two people’s groups LM is the best

choice; in the other cases the two methods are com-

parable and show a similar performance. We fore-

see, that our utility function will improve its effec-

tiveness proportionally to the group size: the larger is

the group, the greater will count altruism in the ﬁnal

decision. Hence, in future works, we should try to en-

courage users to create larger groups in order to better

support our hypothesis.

Finally, we could study how to use other person-

ality factors to build another utility function. We saw

Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study

that extraversion is correlated to the leadership of a

group, so in a decision process, it could be very criti-

cal. Furthermore, even openness could be decisive in

such cases, because it can have the same weight of the

agreeableness in our function. Open people are glad

to try new experiences, so they could agree to view a

movie for which the recommender system predicts a

low value for them, but the opposite for other mem-

bers.

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