This document summarizes a research paper on a chat-based group recommender system for tourism called STSGroup. The system allows groups of users to discuss potential tourism activities together through a chat interface. It uses the preferences revealed by users during their discussion to update its understanding of each user's preferences and make recommendations. An evaluation with real users found the system scored well on usability and recommendation quality measures and users were generally satisfied with the choices it suggested. The researchers plan to further test the recommendation algorithm using simulated data and improve the system usability.

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A Chat-based Group Recommender
System for Tourism
Thuy Ngoc Nguyen and Francesco Ricci
Free University of Bozen-Bolzano, Italy
{ngoc.nguyen, fricci}@unibz.it

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Agenda
• Introduction
• Related Work
• Application Scenario
• Recommendation Logic
• Experimental Evaluation and Results
• Conclusions

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Group Recommender Systems (GRSs)
• GRSs support a group of users in making decisions
when considering a set of alternatives
Where to eat
together?
Where to go
together this
weekend?

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Problem of GRSs
• Most of GRSs use only users’ preferences acquired
before the actual decision making process
• But, …
– Users’ preferences are dynamic (Masthoff, 2015)
– The decision process adopted by a group determines
the quality of the decision output (Forsyth, 2014)
– Group preferences are constructed during the
decision making process (Delic et al., 2016)
How to acquire and identify the true users’ preferences
when they are a part of a group?

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Agenda
• Introduction
• Related Work
• Application Scenario
• Recommendation Logic
• Experimental Evaluation and Results
• Conclusions

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GRSs in Tourism (1)
Intrigue
(Ardissono, L., Goy, A., Petrone, G., Segnan, M. and Torasso, P., 2003)

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GRSs in Tourism (2)
Travel Decision Forum
(Jameson, 2004)

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GRSs in Tourism (3)
Where2eat
(Guzzi, F., Ricci, F. and Burke, R., 2011)

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GRSs in Tourism (4)
Choicla
(Stettinger, M. and Felfernig, A., 2014)

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Agenda
• Introduction
• Related Work
• Application Scenario
• Recommendation Logic
• Experimental Evaluation and Results
• Conclusions

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STSGroup
South Tyrol Suggests
(Braunhofer et al., 2014)
South Tyrol Suggests for Group
- STSGroup

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STSGroup (1)
Companion Management
• Search by user name
• Send connection requests
• Tag companions

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STSGroup (2)
Group discussion
• Exchange messages
• Propose items that are
thought to be suitable
for the group

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STSGroup (3)
• Best, like, or dislike
• Notify users when other
members change their
preferences
• Tag proposals with
comments and emoji
Proposal Evaluations

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STSGroup (4)
• Instruct how to better use
system functions
• Show recommendations
for the group
• Suggest one item, among
the proposed ones, the
group should choose
Recommendations &
Suggestions

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Agenda
• Introduction
• Related Work
• Application Scenario
• Recommendation Logic
• Experimental Evaluation and Results
• Conclusions

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f (u,i) = wk
(u)
xk
(i)
k=1
n
∑
Group Recommendations
• Main idea: infer user’s preferences by observing
their evaluations during the group discussion
• Define a user utility function and assume users prefer
items with larger utility
1 if item i has feature k-th,
otherwise 0
The importance user u assigns to the k-th feature

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Phase 1: Before joining a group
• Identify the importance that user u assigns to the item
features based on ratings
– give more weights to features representing items that
have received high ratings from the user u

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Phase 2: During a group discussion
• If user u prefers item i and dislikes item i’, then infer
the constraint f(u, i) > f (u, i’)
– u likes a POI described by the features “castle” and
“fortress”, and dislikes one having the feature
“swimming” 
• Find the utility vectors that
– satisfy the inferred constraints
– align as closely as possible to the aggregated utility
vector of the group
( )( ) ( ) ( )u u u
castle fortress swimmingw w w+ >

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Phase 3: Update
• Linearly combine original user utility vector (Phase 1)
with the user utility vector reflecting the preferences
acquired in the group discussion (Phase 2)
the updated utility vector
• Identify the group utility vector
w(G)
= α(u,G)w(u)
u∈G
∑
• a non-negative coefficient associated to user u in group G
• the more feedback u provides, the higher coefficient

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Agenda
• Introduction
• Related Work
• Application Scenario
• Recommendation Logic
• Experimental Evaluation and Results
• Conclusions

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Evaluation
• A controlled live user study: 15 participants (3 groups
of two and 3 groups of three)
– System Usability Scale (SUS), compared with a
benchmark (Bangor et al., 2008)
– Perceived recommendation quality (Knijnenburg et al., 2012)
– Perceived choice satisfaction (Knijnenburg et al., 2012)

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Results (1)
Use one-sample t-test as suggested in (Sauro & Lewis, 2012)
•SUS average score of STSGroup: 76, 𝜎 = 7.89
•One sample t-test: t = 4.42 and p = 0.001, more than 99% confidence:
STSGroup’s score > benchmark = 67

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Results (2)
Statement
Strongly
agree
Agree
Neither disagree
nor agree
Disagree
Strongly
disagree
RecQual1 26.7% 60.0% 13.3% 0.0% 0.0%
RecQual2 33.3% 53.4% 13.3% 0.0% 0.0%
RecQual3 0.0% 0.0% 6.7% 53.3% 40.0%
RecQual4 0.0% 73.3% 20.0% 6.7% 0.0%
RecQual5 0.0% 0.0% 6.7% 60.0% 33.3%
RecQual1: “I liked the final choice suggested by the system”
RecQual2: “The final choice recommended by the system was well-chosen”
RecQual3: “I didn’t like the suggested final choice”
RecQual4: “The new item recommendations for a group,
excluding the proposed items were relevant”
RecQual5: “I didn't like any of the recommended new items”

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Results (3)
Statement
Strongly
agree
Agree
Neither disagree
nor agree
Disagree
Strongly
disagree
ChoiceSat1 6.7% 80.0% 13.3% 0.0% 0.0%
ChoiceSat2 6.7% 53.3% 20.0% 20.0% 0.0%
ChoiceSat3 0.0 6.7% 46.6% 40.0% 6.7%
ChoiceSat1: “I was excited about the place that we have chosen”
ChoiceSat2: “The chosen place fits my preference”
ChoiceSat3: “I didn’t prefer the chosen place, but it was fair”

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Agenda
• Introduction
• Related Work
• Application Scenario
• Recommendation Logic
• Experimental Evaluation and Results
• Conclusions

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Conclusions
• Support the group decision making process with a
group chat environment
• The proposed algorithm exploits user’s feedback to
during the discussion  update users’ preferences
• In the future
– Investigate the behavior of the proposed model by
conducting offline experiments on simulated data
– Enhance the usability of system

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