=Paper=
{{Paper
|id=Vol-1945/paper_5
|storemode=property
|title=Towards Chatbots as Recommendation Interfaces
|pdfUrl=https://ceur-ws.org/Vol-1945/paper_5.pdf
|volume=Vol-1945
|authors=Maurizio Atzori,Ludovico Boratto,Lucio Davide Spano
|dblpUrl=https://dblp.org/rec/conf/eics/AtzoriBS17
}}
==Towards Chatbots as Recommendation Interfaces==
https://ceur-ws.org/Vol-1945/paper_5.pdf
Towards Chatbots as Recommendation
Interfaces
Maurizio Atzori1 , Ludovico Boratto2 , Lucio Davide Spano1
1
Department of Mathematics and Computer Science, University of Cagliari
Via Ospedale 72, 09124, Cagliari, Italy
atzori@unica.it, davide.spano@unica.it
2
Digital Humanities, EURECAT
Camı́ Antic de València 54, 08005 Barcelona, Spain
ludovico.boratto@acm.org
Abstract. Providing tourists with the information they need in a timely
and understandable manner is a key objective for promoting cultural
heritage. Currently, tourists often spend only a few days in a city or in
an entire region, so they need recommendations for optimizing the visit.
In this paper, we introduce the objectives of the Tourisitific project, in
which we aim to create a recommender system for travel information,
supporting a lightweight access through chatbots. In this way, we will
support travellers during their visits without burdening them with the
installation of dedicated apps. In addition, we will help small and micro
touristic operators in building their presence on the web with a familiar
and easy to manage interface.
1 Introduction
Providing tourists with the information they need in a timely and understand-
able manner is a key objective for promoting cultural heritage. Currently, tourists
often spend only a few days in a city or on an entire region, so they need rec-
ommendations for optimizing the visit. For building such recommender systems,
we need data for describing points of interest, accommodations, etc.
In particular contexts, e.g., in Sardinia or other southern regions in Italy,
most operators have small activities, such as a tavern or a bed and breakfast
and the organization team includes only two or three people. Such dimensions do
not guarantee resources and knowledge for working adequately on the web and
ensure the social network presence. According to a recent study on trip activity
planning [10], two thirds of travellers in USA uses the Internet as the main
resource. A large majority considers the web essential for planning a vacation,
including paths, point of interest (POI), transportation, information on events,
etc.
Nowadays, we have many online services, which allow people to organize parts
of their journey such as e.g., the overnight stay with Booking.com or Expedia.
They usually offer a vertical and centralised approach focusing on a single service,
thus requiring operators to insert and manage the information in the system in
Towards Chatbots as Recommendation Interfaces 27
a dedicated fashion. For small operators, this requires a good amount of effort,
and sometimes this puts them outside the market.
In this paper, we introduce the Touristific (Semantic Search and User-Friendly
Interfaces for e-Tourism) project, which aims at solving this problem by combin-
ing the power of recommender systems with conversational interfaces. It aims
at creating a chat-based client for inserting and requesting information into an
integrated system for planning travels. We discuss the main ideas behind the
project, its workplan and the advancements in the interaction with a recom-
mender system it will explore.
2 Related work
Relevant research for this project is related to the different travelling or geo-
graphic ontologies [4, 3, 6]. They have been used in isolation, without integrating
them into a single source. For tourist information, a recent attempt to collect ho-
mogeneous data is Wikivoyage (http://www.wikivoyage.org/), which accepts
only text organised in a language-dependent way.
In recent years, different ontology alignment and more general semantic
matching techniques have been discussed in the literature. The more relevant
for our work are recent studies in RDF property matching [8, 7]. Specific search
interfaces for tourism related data have been analysed in [5, 1].
Besides the semantic data analysis, the project will leverage on context-
aware recommender systems that provide services according to e.g., the current
user’s position, time, proximity, etc. In [3], the authors describe a touristic guide
that recommends travel services (e.g., restaurants, hotels and transportation) ac-
cording to the context. In [9], Bayesian networks are exploited for recommending
restaurants. They calculate the estimated appreciation weighting the conditional
probability related to the different restaurant characterizing attributes. A collab-
orative filtering approach for recommending activities and places while visiting
a city is described in [2, 11].
3 Overview of the Touristific project
The Touristific project has two main practical objectives. On the one hand, it
aims at providing a unified platform for tourists that proposes personalised paths
while maintaining a simple interface. On the other hand, it will support small
and micro touristic operators in building a good presence on the web, providing
at least the most useful for the recommender system.
We plan to use an innovative interaction approach for this problem, which is
based on chatbots for the most used messenger applications (e.g., Whatsapp and
Telegram). Since it is reasonable assuming that all operators are familiar with
such applications, this could be a good metaphor for guiding them in creating
the contents. In addition, we aim at exploiting the same interface also for pro-
viding recommendations. This will avoid installing specific client applications in
tourists’ mobiles, which may difficult to find. We will also integrate proximity
28 M. Atzori et al.
interactions based on Bluetooth beacons for simplifying the access to informa-
tion on the go, according to the tourist position with respect to the different
point of interest (POI).
The recommendation of personalised visit packages needs to face three main
challenges. The first is data integration, since we do not have a specific standard.
Our solutions is to apply semantic-web technologies for this purpose, such as
automatic ontology building and semantic annotation. The second challenge is
the personalisation itself: we need to build a system able to respond in real-time
to the constraints that user may express through the chat application, while
considering activities and places that match their preferences. Finally the third
challenge is designing a usable and effective interaction, balancing the need of
information for having better recommendation with a reasonable interaction that
would not overwhelm the user.
3.1 Semantic data integration
The project aims at developing techniques for aggregating and integrating het-
erogeneous information, taking it from sources that are neither synchronised nor
structured. We rely on semantic annotations for solving differences among the
open data repositories, which consists of inserting tags assigning a specific mean-
ing to a particular piece of information, which are independent from the specific
data syntax or transferring protocol.
The goal is having a common ontology with an automatic process, simplify-
ing the inclusion of further sources and minimizing human interventions: even
using RDF triples for representing knowledge, without a constant update the
information becomes obsolete soon. Adaptive techniques applied to both struc-
tured and unstructured sources will provide such data automatically, without
the need of expensive human data entry or cleaning.
The result will be a homogeneous view of data and its structure, which will
be both inspected for providing the personalised packages.
3.2 Automatic recommendation of personalised packages
With the information coming from the different integrated sources, we will define
and fine tune a set of recommendation algorithms that will take into account the
user’s profile and context-dependent attributes: position, social network connec-
tions, weather conditions, the date and time of the visit. On the one hand, we
want to maximise the user’s satisfaction; on the other hand we want to optimise
the visit order in the point of interest.
In the process of generating personalised packages, the context puts a set
of constraints. For instance, it must consider the working hours in a specific
POI, the number of people that is able to accommodate simultaneously, etc.
In addition, users may express constrains through their preferences (e.g., arts,
cuisine, music etc.) both as a single or in a group with other people they are
travelling with.
Towards Chatbots as Recommendation Interfaces 29
The recommendation algorithm should include means for weighting all these
constraints in creating a package, and estimating the degree of interest for the
user and the group for each item included. It is also important to represent why a
specific POI has been recommended, in order to explain it to the user and allow
him/her to modify his/her system internal representation in case the result is
not satisfactory for them.
3.3 User Interface
In order to guarantee the effectiveness of the approach, the recommendation
and the semantic analysis must be supported by a friendly user interface for
both inserting and consuming the personalised packages. We aim at exploiting
a chatbot for both purposes.
For creating the annotated information, the dedicated chatbot will reply to
questions on how to insert specific pieces of information inside the system. For
instance, the user may ask the bot how to insert the description of a B&B room
and the bot will reply with a short description of the content needed (e.g., a
name for the room, a short description and some photos). In addition, it will
support the user in inserting contents through a guided dialogue, with a sequence
of bot initiated questions that the operator will answer through a mobile phone.
In our example, the description of the B&B room will be requested by the
bot asking “Please, send me some text describing the room”. The operator will
write a short text or, in case s/he would need help in identifying how to write
such text, s/he may ask a concrete example to the bot. After that, it will ask
multimedia content for better advertising the room, asking e.g., “Send me some
pictures or a small video now”. The operator will provide them through further
messages. Finally, the bot will ask information for completing the content with
semantic annotations. The request will minimise the number of questions while
will try to maximise the usefulness of the annotation for the recommendation
task.
In order to have a working interface, the bot must support the operator
in building a mental model corresponding to the information needed by the
system for supporting the potential customers, and the process of creating the
package. Through the answers of the bot, the operator will learn to associate
the possible recommendation outcome to parameters that are understandable for
him e.g., associating the user groups identified by the recommendation algorithm
to specific needs in the offered service. At the same time, s/he should be able
to foresee the effect of a change in the service characteristics in the groups s/he
will receive.
A symmetric interface will support tourists during their visit. It will provide
recommendations both on-demand but also in a proactive way. The bot will
reply to questions in natural language, such as e.g., “Where should we go for
dinner tonight?”.
In addition, the bot will exploit contextual information sensed through the
mobile device for sending specific suggestions during the visit, in order to both
recall the recommendations included in the personalised package, but also for
30 M. Atzori et al.
reacting in real time to different types of changes in the plan. Some of them may
be related to last-moment decision of the user e.g., she was going to a restaurant
but in the middle of the road she decides to stop in a museum she noticed while
walking. The system may update the recommendations in order to find another
place suitable for eating.
Moreover, the bot should react to context changes such as the weather (a
sudden shower may require to go inside a museum rather than going for a walk)
or proximity information. For instance, approaching a specific beacon the user
manifests an implicit interest for the particular object, so the system may use
such information for further recommendations. The bot may ask questions for
reinforce the inference.
4 Conclusion and future work
In this paper, we discussed the main goals of the Touristific project, highlighting
the strict correlation between the semantic web technologies, recommendation
techniques and user interface design that would be needed for supporting real-
time context-aware recommendations. In particular, we discussed the main ideas
that will guide the user interface design and the communication with the under-
lying recommender system.
In future work, we will start a proper requirement elicitation work and we
start creating the first version of the demonstrator.
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