=Paper=
{{Paper
|id=Vol-1905/recsys2017_poster9
|storemode=property
|title=Recommender Systems in the Internet of Talking Things (IoTT)
|pdfUrl=https://ceur-ws.org/Vol-1905/recsys2017_poster9.pdf
|volume=Vol-1905
|authors=Fedelucio Narducci,Marco De Gemmis,Pasquale Lops,Giovanni Semeraro
|dblpUrl=https://dblp.org/rec/conf/recsys/NarducciGLS17
}}
==Recommender Systems in the Internet of Talking Things (IoTT)==
https://ceur-ws.org/Vol-1905/recsys2017_poster9.pdf
Recommender Systems in the Internet of Talking Things (IoTT)
Fedelucio Narducci, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro
Department of Computer Science
University of Bari Aldo Moro
Italy
name.surname@uniba.it
ABSTRACT is capable of adapting its behavior to the user feedback by imple-
In the Internet of Things, smart devices are connected to collect and menting a critiquing strategy proposed in [4]. LOD have already
to exchange data. In our vision, in the Internet of Talking Things, been effectively used in other recommendation scenarios [9]as well
objects such as intelligent fridges will be able to communicate as for other tasks such as cross-lingual information retrieval [7, 8].
with humans to set up preferences and profiling options which In the next Section how the Telegram Bot works and its interac-
allow a personalized usage of the object. In this paper, we present tion with the user are described.
a recommender system implemented as a Telegram Bot, that can
fit with the previous scenario. The system is a movie recommender 2 DESCRIPTION OF THE CHATBOT
which exploits the information available in the Linked Open Data The workflow carried out by the Bot is depicted in Figure 1. In the
(LOD) cloud for generating the recommendations and leading the
conversation with the user. It can be easily seen as an intelligent
component of a connected TV.
ACM Reference format:
Fedelucio Narducci, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
Copyrights held by the authors. Recommender Systems in the Internet of
Talking Things (IoTT) . RecSys 2017 Posters, Como, Italy, August 27-31 (RecSys
2017 Poster Proceedings), 2 pages.
1 BACKGROUND AND MOTIVATIONS
The main distinctive feature of a conversational recommender sys-
tem (CORS) compared to a classical one is its capability of inter-
acting with the user during the recommendation process [3]. The
user provides feedback and tries to get better recommendations. It
is not essential that a complete user profile has been built before
beginning the recommendation process and all preferences have Figure 1: The Bot workflow
been specified upfront by the user. There is a cycle of interactions
between the CORS and the user repeated until the user reaches first step, Preference Acquisition, the Bot asks the user to express
an item of interest. Accordingly, the goal of a CORS is not only to her interests. It asks questions related to entities (e.g, movies and
improve the accuracy of the recommendations, but also to provide persons) and their properties in DBpedia (e.g, genre, role). When the
an effective user-recommender interaction. user starts the interaction, her profile is empty, so the recommender
In this paper we propose a conversational movie recommender system needs to address a classical cold-start problem. The system
system implemented as Telegram Bot (@MovieRecSysBot). Chat- offers the user two different strategies to express her preferences:
bots are a kind of bots which emulate user conversations. The main (i) rating a set of items or properties proposed by the system; (ii)
advantages of using a Telegram bot are that it facilitates the interac- typing the entities or properties she is willing to rate. The first op-
tion of the user by a clean and well-known user interface (the same tion allows the user to express the preferences by tapping buttons.
that people daily use for other purposes on their smartphones), The second option implements an entity recognizer based on the
it does not require credentials since each account is identified in Levenshtein distance [11] by means of a Did you mean function
Telegram by the phone number, and lastly the user can answer (Figure 3 (a)), so that, if the user makes typos, the system is anyway
by tapping a button. The Bot is based on the Linked Open Data able to recognize the right entity or property. The second step is
(LOD) cloud, and more specifically on the properties encoded in the Recommendation. The Bot currently implements PageRank with
DBpedia1 . These properties are exploited by the Bot for eliciting Priors [2], also known as Personalized PageRank. Differently from
user preferences, for providing recommendations as well as for gen- PageRank, which assigns an evenly distributed prior probability to
erating personalized explanations in natural language. The system each node (1/N , where N is the number of nodes), the Personalized
1 http://wiki.dbpedia.org/ PageRank adopts a non-uniform personalization vector by assign-
ing different weights to different nodes to get a bias towards some
RecSys 2017 Poster Proceedings, Como, Italy nodes (in this case, the preferences of a specific user). The algorithm
Copyrights held by the authors. has been effectively used in other recommendation environments
RecSys 2017 Poster Proceedings, August 27-31, Como, Italy F. Narducci et al.
[1]. Figure 2 shows how the user preferences and the DBpedia
properties are represented in a single graph. The algorithm is run
for each user and the assignment of the probabilities to the nodes
has been inspired by the model proposed in [5]. The algorithm
generates a ranking of the items potentially interesting for a given
user. The Bot also implements an Explanation module. Tintarev
and Masthoff [10] point out that explaining a recommendation is
generally intended as justifying the suggestion, but it might be also
intended as providing a detailed description that allows the user to
understand the quality of the recommended item. The Bot is able
to provide these types of explanation. Details about an item can be
obtained by tapping on a Details button (Figure 3 (b)) which shows
information extracted from IMDB on a given movie. The Why?
button implements an explanation algorithm inspired by [6]. The
idea is to use the connections in the LOD-based graph between the
user preferences and the recommended items for explaining why a
given item has been recommended.
Figure 3: A screenshot of the Bot during the training phase
in typing mode (a), and the recommendation phase (b)
new updated graph. Finally, the Bot allows the user to explore and
to update her profile. Through these functions the user can view
the preferences stored in her profile and change them. At the end,
when the profile has been updated, the system will run again the
PageRank and generate a new set of recommendations.
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The node associated to the characteristic the user does not like
(e.g., Quentin Tarantino) will be removed from the graph used by
the PageRank and the recommendation process starts again on the