Reaching Agreement in an Interactive Group Recommender System
Dai Yodogawa
1 a
and Kazuhiro Kuwabara
2 b
1
Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577 Japan
2
College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577 Japan
Keywords:
Group Recommender System, User Model, Agent, Conversation Strategy.
Abstract:
For a group recommender system, it is important to recommend an item that can be accepted by all group
members. This paper proposes a group recommender system where preferences elicited from group members
are used to select an item that is agreeable to all of them. In this system, an agent that corresponds to each
group member manages estimation of the corresponding user’s preferences. Virtual negotiation is conducted
among these agents to find an appropriate item to recommend, and the selected item is presented to group
members. If it is not accepted, the system asks members to relax their requirements and accordingly updates
its recommendation. We report and discuss the results of simulation experiments with different personality
types of conflict resolution and different conversation strategies.
1 INTRODUCTION
With ever-increasing information available, recom-
mender systems have become part of our everyday
lives. Many recommender systems target an individ-
ual user, but much research also has focused on sys-
tems that target a group of people (Ricci et al., 2015).
For a group recommender system, a recommendation
can be generated by (1) aggregating users’ profiles
to make a profile as a group and applying a recom-
mender algorithm for an individual user, or (2) aggre-
gating items’ rankings or ratings for each user to pro-
duce a recommendation for a group (Felfernig et al.,
2018).
For certain application domains, such as finding a
group travel destination, it is important to recommend
an item that all the group members can accept. For
such a case, the concept of negotiation is a promis-
ing approach that makes use of users’ rankings or rat-
ings for each item (Bekkerman et al., 2006). For each
user, an agent is placed that has the preference infor-
mation of the corresponding user and acts on behalf of
the user. Negotiation is often conducted among these
agents to find an agreed item.
We develop an interactive group recommender
system that asks for users’ requirements and feedback
on a recommended item (Yodogawa and Kuwabara,
2019). By asking users to relax their requirements,
the system attempts to find an item that all the group
members can accept.
a
https://orcid.org/0000-0002-0319-9225
b
https://orcid.org/0000-0003-3493-1076
In this paper, we extend our system to include user
agents. Here, an agent is not meant to act on behalf of
the corresponding user, but rather, the agent is placed
inside the recommender system and it manages the es-
timated values of a user’s preferences. By introducing
these agents, a recommender system can simulate ne-
gotiations among users inside the system and produce
an item that might be acceptable to all the users.
When the produced item is actually accepted by
the users, the recommendation process ends. Other-
wise, the system asks the users to relax their require-
ments. Based on their responses, the system updates
its estimates of users’ profiles, selects a new item for
recommendation, and presents it to the users. This
process continues until the selected item satisfies all
the users or no further items can be recommended.
In this paper, we consider a conversation strategy
for the system to effectively reach an agreement. To
evaluate the proposed system, we conduct simulation
experiments with a user model that is based on per-
sonality types of conflict resolution.
The remainder of this paper is organized as fol-
lows: Section 2 describes related work, Section 3
describes our proposed agent-based mechanism for
a group recommendation system, Section 4 presents
and discusses the results of simulation experiments to
examine the characteristics of the proposed system,
and Section 5 concludes the paper and discusses fu-
ture work.
Yodogawa, D. and Kuwabara, K.
Reaching Agreement in an Interactive Group Recommender System.
DOI: 10.5220/0009160502950302
In Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) - Volume 1, pages 295-302
ISBN: 978-989-758-395-7; ISSN: 2184-433X
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
295

2 RELATED WORK
Several studies have applied a multi-agent system to a
group. In the PUMAS-GR system, a user agent is set
up for each user, and agents negotiate with each other
using a monotonous concession protocol (Villavicen-
cio et al., 2016). It was reported that the PUMAS-
GR system found a better solution compared with
the approach of aggregating the users’ ranking of
items. Similarly, agents have been introduced to elicit
group preferences (Garcia et al., 2011), where a user
agent models a human user’s preferences. To aggre-
gate preferences, voting procedures and negotiation
among agents are utilized.
In contrast to these systems, in the proposed sys-
tem, user agents do not act for a human user. Rather,
they mimic the negotiation between users with their
estimated preferences.
3 RECOMMENDATION
MECHANISM
3.1 Data Model
In the proposed recommender system, we assume
there are m items, from which an item is selected for
recommendation. Each item t
i
(1 ≤ i ≤ m) has n at-
tributes that describe the item’s features. We also as-
sume that there are K users in the group. User u
k
(1 ≤ k ≤ K) will input their requirements r
(u
k
)
j
for
each attribute j. Item t
i
is assumed to have an evalua-
tion function eval
(t
i
)
j
for each attribute j, which takes
each user’s requirement value for attribute j, r
(u
k
)
j
, as
its parameter and returns an evaluation score about
the user’s satisfaction of item t
j
regarding attribute
j. The range of the evaluation function is assumed
to be between 0 and 1, where 0 means that the user
does not like the item at all regarding the particular
attribute. Note that the domain of the evaluation func-
tion is set differently for each attribute. For example,
attributes describing price and distance may have dif-
ferent value ranges.
User u
k
’s utility value for item t
i
, U
(u
k
)
(t
i
), is cal-
culated as follows:
U
(u
k
)
(t
i
) =
∑
n
j=1
(w
(u
k
)
j
· eval
(t
i
)
j
(r
(u
k
)
j
))
∑
n
j=1
w
(u
k
)
j
,
where w
(u
k
)
j
represents a weight to model the impor-
tance of attribute j is to user u
k
’s utility value. In
addition, we assume that user u
k
has a threshold T
(u
k
)
User
User
User
Group Recommender System
User
Agent
User
Agent
User
Agent
...
...
Negotiation Manager
Figure 1: Overview of the proposed system.
that needs to be satisfied when a recommended item
is accepted by the user. That is, user u
k
is assumed to
accept item t
i
whose utility value for user u
k
equals or
exceeds the threshold (U
(u
k
)
(t
i
) ≥ T
(u
k
)
).
3.2 Recommendation Flow
Figure 1 shows the overall structure of the proposed
system. We place a user agent for each user. This
agent manages the estimation of the corresponding
user’s preferences. Note that the agent is placed in-
side the recommendation system virtually and it does
not know the user’s exact preferences and does not
act on behalf of the user. Rather, it is placed to
simulate the negotiation process inside the recom-
mender system and to produce a recommended item.
Since the proposed mechanism simulates negotiation
among agents to find an item to recommend, we also
placed a negotiation manager that mediates agents in-
side the system.
Figure 2 shows the overall control flow of the pro-
posed recommendation mechanism. First, the system
asks each user their requirements for each attribute.
Based on their responses, an agent is set up for each
user inside the system. The agent knows the current
value of the user’s requirements from the conversation
between the system and the user, but it is assumed that
the agent does not know the threshold and the weight
in calculating the items’ utility for each user. The
agent holds estimations of these values. They are ini-
tialized to pre-determined values and updated as the
system and the user interact.
Next, we simulate the negotiation process among
the user agents to determine which item to recom-
mend. For a two-agent case, if the monotonic con-
cession protocol is adopted for the negotiation and
the agents behave according to the Zeuthen strategy,
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
296

it is known that they reach an agreement where the
product of their utility values is maximized (Zeuthen,
1930). Using this finding, the one step protocol was
devised where the agreement between two agents is
selected as the one that maximizes the product of the
two agents’ utility (Rosenschein and Zlotkin, 1994).
In the proposed system, we use an extended one
step protocol for multiple users (Endriss, 2006). The
user agent in the system calculates the estimated util-
ity of items for its corresponding user and reports the
values to the negotiation manager in the system. The
negotiation manager aggregates the estimated utility
values and selects the item that maximizes the prod-
uct of all users’ estimated utility values. That is,
argmax
i
∏
k
b
U
(u
k
)
(t
i
),
where
b
U
(u
k
)
(t
i
) denotes an estimated value of
U
(u
k
)
(t
i
). If there are multiple items with the largest
product of utility values, we select one of these at ran-
dom.
An item should satisfy the user when the item’s
true utility value for the user equals or exceeds the
threshold of the user. Since the system only knows
the estimated utility values and threshold of a user, the
system cannot determine whether the selected item
will be accepted by the user. The system presents
the item and asks the user if they are satisfied with
it. Here, we assume that the goal of the system is to
find an item that is acceptable to all users in the group.
If all the users are satisfied, the recommendation pro-
cess ends. If there is a user who is not satisfied, an-
other recommendation is sought.
3.3 Exploring Phase
When a user responds that they are not satisfied with a
recommended item, the system updates its estimated
threshold for the user. For example, assuming that
item t
r
is recommended and user u
k
is not satisfied
with t
r
, the estimated threshold
b
T
(u
k
)
is updated to the
estimated utility of t
r
for user u
k
,
b
U
(u
k
)
(t
r
).
To explore other possibilities for a recommended
item, the system asks if the requirement for the item
can be relaxed. If the user agrees to relax their re-
quirements, utility values for items may change. A
previously recommended item might be accepted, or
another item could be recommended.
More specifically, for the recommended item
t
r
and user u
k
, the system finds attribute l that
has the lowest evaluated score. That is, l =
argmin
j
b
w
(u
k
)
j
·eval
(t
i
)
j
(r
(u
k
)
j
)
∑
n
j=1
b
w
(u
k
)
j
. The system asks the user
to relax the requirement for attribute l, r
(u
k
)
l
. Note
start
Initializes user agents
Simulates negotiations
among agents
Solution found?
failure
No
Presents the solution to
users
All agreed?
success
Yes
Updates agents through
interaction with users
No
Yes
Figure 2: Overall control flow of the recommendation
mechanism.
that since the system does not know the exact value
of weight w
(u
k
)
j
, its estimated value is used for calcu-
lation.
After attribute l is determined, the system asks
user u
k
to relax the requirement for attribute l, r
(u
k
)
l
.
When doing so, the system may suggest how much
the requirement r
(u
k
)
be relaxed. We call this a hint
in the sense that the user can decide how much the
requirement should be relaxed to reach an agreement.
This value is determined so that the utility of the rec-
ommended item for user u
k
, U
(u
k
)
(t
r
), will become
higher than user u
k
’s threshold, T
(u
k
)
.
One possible heuristic to determine the amount of
concession to request is as follows. Since the esti-
mated utility value of the recommended item for the
user is the weighted average of the evaluation value of
all the attributes, the system would ask the user to re-
lax the requirement for attribute l so that the evaluated
value of attribute l matches the average value.
A user responds to such a request from the sys-
tem, either by rejecting the request or changing the re-
quirement for attribute l. The system then updates the
information about the user and recalculates the utility
values of possible items for all the users. As in the ini-
tial round, negotiation among user agents is simulated
and an item to be recommended next is calculated.
To avoid falling into an infinite loop, the system
will recommend the same item at most L times. Items
Reaching Agreement in an Interactive Group Recommender System
297

that have been recommended L times are removed
from possible items to be recommended. When the
system cannot find an item because there are no re-
maining items for recommendation, the recommen-
dation process ends in failure.
4 SIMULATION EXPERIMENTS
To investigate the characteristics of the proposed
mechanism, we conducted simulation experiments as
follows.
4.1 Dataset
We defined six attributes (n = 6) to describe an item
assuming it is a sightseeing spot, as shown in Table 1.
Using these attributes, we randomly generated 120
items (sightseeing spots) (m = 120) for the simula-
tion experiments. The range of values for each at-
tribute are also provided in Table 1. Here, access,
landscape, crowdedness, and barrier free are assumed
to be five-star review scores. Thus, the value range of
these attributes is between 1 and 5.
4.2 User Model
4.2.1 Parameters
In the simulation experiments, a user is characterized
by the following parameters.
• Initial Requirements for each attribute (r
(u
k
)
j
)
The system first asks each user for their require-
ments for each attribute. The requirements may
change during the recommendation process.
• Weight for each attribute (w
(u
k
)
j
)
The utility value for an item is calculated as the
weighted average of the score of all the attributes
of the item. The weights may be different from
user to user.
• Initial Threshold
When the system presents a recommended item
(t
r
), a user’s utility value (U
(u
k
)
(t
r
)) is calculated.
When it equals or exceeds its threshold (T
(u
k
)
), the
user should accept the item.
• Threshold Decay
The criteria for accepting a proposal during a ne-
gotiation tend to become lower after many rounds
of negotiations. Thus, to simulate such behav-
ior, we define a parameter to decrease the thresh-
old after each time the system presents a recom-
mended item.
• Concession Factor
When the system asks a user to relax the require-
ment for a particular attribute, the user may relax
the requirement according to the concession fac-
tor. Note that for avoiding users, the concession
factor is irrelevant since any item is acceptable to
them as their threshold is set to 0.
4.2.2 Evaluation Function
To calculate the utility of items t
i
for user u
k
, we de-
fined the evaluation function eval
(u
k
)
j
for attribute j as
follows. If j is price or distance,
eval
(t
i
)
j
(r
(u
k
)
j
) =
(
r
(u
k
)
j
t
i
[ j]
if r
(u
k
)
j
≤ t
i
[ j]
1 otherwise,
where t
i
[ j] represents the value of attribute j of item
t
i
.
If j is a review-type attribute such as access or
landscape,
eval
(t
i
)
j
(r
(u
k
)
j
) =



1 if r
(u
k
)
j
≤ t
i
[ j]
5−r
(u
k
)
j
5−t
i
[ j]
otherwise.
4.2.3 Personality Type
To determine these parameters, we used four types of
interpersonal conflict-handling behavior as described
in the Thomas-Kilmann Conflict Model (Kilmann and
Thomas, 1975). This model considers personality
types on two axes: cooperativeness and assertiveness,
as shown in the left two columns of Table 2. This
model is often used to evaluate group recommender
systems (e.g., (Rossi et al., 2017), (Nguyen et al.,
2019)).
In the simulation experiments, we set a higher
threshold for the personality types with high as-
sertiveness. For the personality types with high co-
operativeness, we set a higher concession factor and
threshold decay.
4.2.4 Simulation Conditions
We set up three users (u
A
, u
B
, and u
C
) for the sim-
ulation experiments. Their initial requirements are
shown in Table 3. These values were set to be rela-
tively strict so that an agreement is less likely to be
reached in the first round. Among the three users,
u
A
has the strictest requirements and u
C
has the least
strict requirements. All weights (w
(u
k
)
j
) used to calcu-
late the utility values were set to 1.
A user is supposed to accept a recommended item
if the utility value of the item for the user equals or
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298

Table 1: Defined attributes for describing items.
Attribute Description Value range
price amount of money expected to be needed 1000 – 10000
distance distance from the nearby airport 10 – 120
review
score
access how easy to access 1 – 5
landscape how beautiful its landscape is 1 – 5
crowdedness how not crowded it is 1 – 5
barrier free how easy it is for people with disabilities to visit 1 – 5
Table 2: User personality type and parameters.
Personality type Cooperativeness Assertiveness
Initial Threshold Concession factor
threshold decay price distance review ratio
collaborating high high 0.8 0.01 250 10 0.5 0.5
accommodating high low 0.7 0.01 100 10 0.25 1.0
competing low high 0.9 0 0 0 0 0.0
avoiding low low 0 0 – – – –
Table 3: Initial requirements of users in the simulation.
Attribute
User
u
A
u
B
u
C
price 500 500 1000
distance 10 20 20
review
score
access 4 4 4
landscape 5 4 4
crowdedness 5 5 5
barrier free 5 5 4
exceeds the user’s threshold. To reflect the personal-
ity types in the simulation model, the initial threshold
was set according to the personality types as shown
in Table 2. In addition, the threshold is decreased
each time the item is presented by the amount speci-
fied as threshold decay, which is set to 0.01 for types
whose cooperativeness is high (Table 2). For other
personality types, this value is set to 0, meaning that
the threshold does not change.
4.2.5 Requirement Concession
The system asks a user to relax the requirements for a
particular attribute when the user does not accept the
recommended item. The user may reject this request
or relax the requirement according to the personality
type. In the simulation experiments, when the sys-
tem asks to relax the requirement without providing a
hint, the user is supposed to change the requirements
as specified in Table 2. For example, if the user’s per-
sonality type is collaborating, and they are asked to
relax the requirement of pr ice attribute, they increase
the requirement value by 250, and if they are asked to
relax the requirement of access, which is one of the
review attributes, they decrease the requirement value
by 0.5.
When the system asks the user to relax the re-
quirement with a hint specifying the amount of con-
cession to make, a user should relax the requirement
by the hint amount multiplied by a concession factor
ratio, which is specified for each personality type as
shown in Table 2. The accommodating user relaxes
the requirement as suggested by the system, whereas
the collaborating user only relaxes the requirement
for half of the amount suggested by the system, and
the competing user ignores the request and does not
change the requirement. Note that for the avoiding
user, no concessions are needed as they accept any
item.
We assigned one personality type from four pos-
sible types to each user. Since there were three users
(u
A
, u
B
and u
C
) and four personality types, there are
64 (= 4 × 4 × 4) cases to consider. For each case, we
ran a simulation of both conversation strategies with
and without hints when the system asks a user for a
concession about a particular attribute. In addition,
the system is supposed to recommend the same item
at most twice (L = 2) in the simulation experiments.
4.3 Results
Among 64 cases, agreement was reached for 27 cases
for the conversation strategy without hints and the
same 27 cases for the strategy with hints. Cases with
even one competing user failed to reach an agreement
in the simulation experiments. For the 27 cases with
an agreement, we plotted the number of rounds before
the agreement is reached for two strategies (Figure 3).
As seen in this chart, for about half of these cases,
the number of rounds was less when a hint was used
when requesting the concession. The effect of a hint
is large especially for cases (p1, p2, . . . , p15) that in-
volve at least one collaborating user except where the
only collaborating user is not u
C
, which has the least
Reaching Agreement in an Interactive Group Recommender System
299

0
5
10
15
20
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27
Number of rounds before an agreement is reached
Simulation case
without hints
with hints
Figure 3: Effect of giving hints on the number of rounds before an agreement is reached for the 27 agreed cases.
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27
Median value is shown as a box.
Maximum and minimum values are shown as whiskers.
Decrease in the user's threshold
Simulation case
without hints
with hints
Figure 4: Effect of giving hints on the decrease in the user’s threshold when an agreement is reached for the 27 agreed cases.
strict initial requirements. This reflects that a collab-
orating user with strict initial requirements tends to
need to make more concessions to reach an agree-
ment, and hint is effective for such cases.
To evaluate the quality of the solution obtained
as an agreement, we examined how much the user’s
threshold was decreased when the agreement was
reached compared with the initial threshold. Figure 4
shows the median value of the difference between the
final and initial thresholds for the three users in each
simulation case. This chart also shows the maximum
and minimum of the difference as whiskers. As seen
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
300

0.0
0.1
0.2
0.3
0.4
0.5
p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20 p21 p22 p23 p24 p25 p26 p27
Utility of the item agreed
(product of all users' utility calculated with the initial requirements)
Simulation case
without hints
with hints
Figure 5: Effect of giving hints on the utility of an agreed item for the 27 agreed cases.
in this chart, the reduction in the number of negotia-
tion rounds naturally leads to less concession required
in the negotiation process (that is, less decrease in the
threshold value compared with the initial one). Note
that since the initial threshold of an avoiding user is
set to 0, an avoiding user’s threshold cannot be de-
creased further. Thus, in a simulation case that in-
volves at least two avoiding users, the median value
shown in the chart is inherently 0, even when the max-
imum difference is greater than 0 (p5, p11 and p16).
In addition, we calculated a utility value (the prod-
uct of all users’ utility calculated with the initial re-
quirements) of the agreement reached in these cases,
as shown in Figure 5. The figure demonstrates that
when agreement is reached in fewer rounds, the utility
value tends to be higher. This indicates that even with
fewer rounds, the quality of the solution (or agree-
ment) does not decrease. The results also indicate
that giving hints as a conversation strategy is gener-
ally effective from the perspective of both the number
of rounds before reaching agreement and the quality
of the obtained solution.
5 CONCLUSION
This paper described an interactive group recom-
mender system where agents that correspond to users
are placed inside the system. Each agent is expected
to hold the estimated values of the corresponding
user’s profile and is used to conduct virtual negotia-
tion to find a recommended item.
The characteristics of the proposed system were
examined through simulation experiments that intro-
duced four personality types of negotiation and two
conversation strategies. By adding hints when the sys-
tem asks a user to relax a requirement, the number of
items to be presented before reaching the agreement
can likely be reduced while the quality of the agree-
ment is maintained.
There is much work to be done. For example, cur-
rently when a user rejects the recommended item, the
system only asks them to relax a requirement. If the
system can ask the user why they rejected the item and
make use of the response to find another item to be
recommended, an agreement could be reached faster.
We may also need to consider how to select the item
to recommend. Currently, the item that maximizes
the product of the users’ utility values is selected, but
other types of social welfare functions could also be
used. Finally, we only simulated a three-user case.
We plan to increase the number of users to see the
effect of different compositions of user personality
types.
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