=Paper=
{{Paper
|id=Vol-2960/paper10
|storemode=property
|title=GUApp: a Knowledge-aware Conversational Agent for Job Recommendation (Short paper)
|pdfUrl=https://ceur-ws.org/Vol-2960/paper10.pdf
|volume=Vol-2960
|authors=Giovanni Maria Biancofiore,Tommaso Di Noia,Eugenio Di Sciascio,Fedelucio Narducci,Paolo Pastore
|dblpUrl=https://dblp.org/rec/conf/recsys/BiancofioreNSNP21
}}
==GUApp: a Knowledge-aware Conversational Agent for Job Recommendation (Short paper)==
https://ceur-ws.org/Vol-2960/paper10.pdf
GUApp: a Knowledge-aware Conversational Agent for Job
Recommendation
Giovanni Maria Biancofiore1,2 , Tommaso Di Noia1 , Eugenio Di Sciascio1 , Fedelucio Narducci1
and Paolo Pastore1,2
1
Polytechnic University of Bari, Bari, Italy
2
Corresponding authors
Abstract
GUapp is an ecosystem for job-postings search and recommendation for the Italian public administration. Its main goal is
to match user skills and requests with job positions available on the Gazzetta Ufficiale website, offering recommendation
services in a conversational setting. G u a p p ’s dialogues are modelled employing a domain-specific Knowledge Graph, which
improves the users’ natural language interaction with the app as well as the user experience. Thanks to that, the search
and recommendation process becomes incremental and the user can dynamically provide her preferences at each stage of
the interaction. In this paper, we present GUapp and its overall architecture, besides the functioning of the conversational
agent that dialogues with the user by exploiting a custom-designed Knowledge Graph. We also show a running example that
outline how GUapp models users and provides them effective recommendations through natural language conversations.
Keywords
Recommender System, Public Administration, Knowledge Graph, Conversational Recommender System
1. Introduction that generally do not take into account the user’s past
preferences, and only retrieve the most relevant docu-
The information overload is a well-known problem that
ments based on the current user query. Recommender
impacts the digital experience of users when they need
Systems (RS) are Information Filtering tools for suggest-
to find interesting items in a large set of possible op-
ing services and items tailored to the specific users’ char-
tions [1]. That is the case of looking for a book to read,
acteristics and requests. In our case, the list of job calls
a smartphone to buy, a TV series to watch, and so on.
is daily updated and provided to the user depending on
Users especially perceived the same issue when they are
her past preferences.
looking for a new job, where the only available strategy
G U a p p offers a natural-language based interaction
is to search for interesting job calls. Moreover, job calls
through a chatbot, which allows the user to define her
have a limited period for applying. For this reason, it is
interests, describe her skills, and filter out results that did
crucial to constantly look for attractive job openings. In
not match with her requirements. Moreover, our system
this scenario, a system that only allows to search by a
leverage the felt issues of cold-start and the possible lack
query composed of a set of relevant keywords can make
of items to suggest with a Conversational Agent which
this task an ordeal for users.
interacts with users exploiting a domain-specific Knowl-
The G U a p p platform has been designed and developed
edge Graph (KG), differently from the previous version of
to find and discover job positions among job offers in the
the tool [2]. The G U a p p KG is obtained by merging some
Italian public administration1 . This problem has been
sub-graphs from state-of-the-art solutions like Dbpedia2
investigated in the literature from two different perspec-
and new triples generated from data scraped from exter-
tives: Information Retrieval and Information Filtering.
nal sources such as the ISTAT3 website. From the latter,
From the Information Retrieval side, there are systems
we have taken information about profession hierarchies
and fields to which jobs belong. Furthermore, we have
3rd Edition of Knowledge-aware and Conversational Recommender built an ontology on which the retrieved facts rely. This
Systems (KaRS) & 5th Edition of Recommendation in Complex KG allows our system to search for new semantically
Environments (ComplexRec) Joint Workshop @ RecSys 2021,
September 27–1 October 2021, Amsterdam, Netherlands
linked user preferences, besides engaging a negotiation
Envelope-Open giovannimaria.biancofiore@poliba.it (G. M. Biancofiore); phase when the proposed calls do not match all the user
tommaso.dinoia@poliba.it (T. Di Noia); requirements. Exploiting the KG relations, GUapp can
eugenio.disciascio@poliba.it (E. Di Sciascio); search for jobs that do not perfectly suit the user prefer-
fedelucio.narducci@poliba.it (F. Narducci); ences but still remaining close to her interests. On this
paolo.pastore1@poliba.it (P. Pastore)
© 2021 Copyright for this paper by its authors. Use permitted under Creative line, conversations can reach a finer grained level of de-
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1 2
All the documents are freely available online at https://www. https://www.dbpedia.org/
3
gazzettaufficiale.it/30giorni/concorsi https://www.istat.it/
tails on job aspects to recommend to users and enhance natural [19]. Accordingly, a CoRS let the system build
its expressiveness as well. the user profile during the interaction, allowing her to
express preferences, by a human-like dialog. The afore-
2. Related Work mentioned task is well suited to Knowledge-based and
KG-based RSs. An example can be found in [20], where
The proposed work is between two principal research
a comprehensive KG is built upon a specific domain to
areas: recommender systems and conversational agents.
lead dialogues and recommendations. CoRS have proved
Recommender Systems (RSs) support the user during the
to be very effective both from the recommendation and
decision making process when she has to decide among
Human-Computer Interaction perspective and they have
a large set of different options. RSs are grouped into
been used in different domains [21, 22]. However, to
two main categories: Collaborative Filtering (CF) and
the best of our knowledge, the G U a p p system represents
Content-based (CB). The CF exploits the user commu-
the first attempt of implementing a CoRS for the job-
nity in order to identify items potentially interesting for
recommendation task.
a given individual, following the intuition that similar
users like similar items. Conversely, CB systems try to
suggest items matching user preferences with items de-
3. The G U a p p ’s Architecture
scriptions [1]. G U a p p implements a CBRS. Actually, the In Figure 1 G U a p p ’s architecture is sketched, composed of
main goal of the RS behind GUapp is to match the user six main components: the Orchestrator, the Chatbot, the
requests/preferences with the textual description of the Recommender System, the User Profiler, the Crawler and
job proposal. A lot of studies investigated the specific the Knowledge Graph.
task of job recommendation [3]. In the past, the most
used approaches for job recommendation were based on
boolean search and filtering [4]. Later, the attention has
been focused on the problem of catching the user pref-
erences and building a user profile. In [5] the authors
propose a system that builds the user profile by passively
detecting click-stream and read-time behaviour of users.
Malinowski et al. [4] proposes a strategy based on multi-
slot user profile in which different information are stored:
demographic data, job experiences, languages, and IT
skills. Similarly, in G U a p p tool this kind of information is
Figure 1: The G U a p p ’s Architecture and its flow of data.
acquired by mean of a KG-driven conversation. Recom-
mending items by means of a set of rules which verify
whether the user tastes are satisfied or not was dealt The Orchestrator manages the interaction between the
at first by the Knowledge based Recommender Systems different components of the system. For example, it in-
[6]. These systems perform reasoning on ontologies and vokes the RS when user asks for receiving a list of job
Knowledge Bases (KBs) to find items that match the user positions based on the information stored in her profile.
preferences. For instance, Carrer-Neto et al. [7] opted In detail, this module leverages the overall flow of data se-
that movies belonging to the same ontological classes lecting the appropriate component to solve specific tasks.
of the items that the user liked in the past has to be rec- We will consider the case on which the Recommender
ommended. Differently, Tarus et al. [8] exploit both System needs to start a negotiation phase with the user.
ontological features and collaborative filtering to provide The Orchestrator will collect data semantically related to
recommendations in the field of learning resources for her profile by querying the Knowledge Graph, that will
some learner targets. be exploited to find other items interesting to the user.
On the other hand, Conversational Agents are software The Chatbot is the component of G U a p p which allows
agents that use natural language to interact with the user. users to interact through natural language. It is imple-
They can be classified in two main classes: end-to-end mented by using DialogFlow4 , a Google platform for
and modular systems [9]. The former typically exploits designing and integrating conversational user interfaces,
Deep Learning techniques for learning a dialog model and it mainly leads conversations with users. For this
from a set of past conversations [10, 11, 12, 13, 14]. Mod- purpose, the chatbot is equipped with an Intent Recog-
ular systems adopt a pipeline-based agent which is com- nizer and an Entity Recognizer, allowing the system to
posed of a set of modules, each with a specific function understand several user requests and retrieve from her
[15, 16, 17, 18]. The main difference between a Conversa- essential data for the recommending task. That is a cru-
tional Recommender System (CoRS) and a traditional RS cial step in order to identify the back-end services to be
is the interaction with the user that is more efficient and
4
https://dialogflow.com/
invoked for accomplishing the request. TheIntent Recog- job features that are crucial for the recommendation task,
nizer analyzes the natural language request searching for besides granting the chatbot to leverage fine-grained
specific goals such as collecting the user preferences, pro- conversations and negotiations with users. To make the
viding new job recommendations, or negotiating with the system more specialized on the job-opening domain, we
user. The Entity Recognizer is invoked in order to check plan to enrich our KG of further facts related to the job
whether the sentence contains mentions to real-world calls an positions recommendation.
entities. It is implemented through Dialogflow as well,
and it is powered by the KG entities. It adopts a fuzzy- 4. Building the Knowledge
matching strategy in order to identify real-world entities Sources
in the user sentence. In the case the match succeeds, the
recognized entity is returned. The more detailed a collection of data about features of
The User Profiler instead collects all the users prefer- items is, the higher is the accuracy of the recommenda-
ences. In detail, we store all the data that they provide tions provided by the system. Following this intuition
during conversations like favourites job locations, pro- and given the conversational configuration on which
fessions etc. When the user signs into the app for the GUapp is built, we found owning a well-structured source
first time, her profile is empty (i.e. cold-start situation), of information an essential requisite. The system can
but thanks to the chatbot the user talks with G U a p p about provide more fine-grained recommendations by using
her skills, wishes, and ambitions, then the system is able a knowledge source that defines aspects users evaluate
to rank job calls by exploiting the provided information. to match their interests. For instance, job location and
The User Profile is actively updated to the new user in- profession that a person could cover represent two main
puts, guaranteeing the recommendations to be adaptive. features that people consider while seeking a new job. At
The Recommender System is another core component the same time, skills and experiences are crucial informa-
of G U a p p . It exploits an Elasticsearch5 index, which stores tion to retrieve the most proper job position for the user.
all the information scraped by the Crawler enriched with The employment of a Knowledge Graph further allows
all the linked entities of our KG. In particular, for each the system to build semantically explicit user profiles.
job call, we automatically search for mentions of the KG That is results in highly interpretable recommendations,
entities or the ontological categories. The Elasticsearch besides leading efficient negotiations in case there is no
documents will store the entity/category label related item that satisfies all the user requirements.
to each discovered mention with a confidence score, es- On this line, we opted to enrich GUapp with a col-
timated with the BM25 algorithm, which shows how lection of Linked Open Data (LOD), suitable for the job
much the labelling process outcomes are reliable. The recommendation task, since their availability in struc-
recommendations will be the job call closest to the user tured non-proprietary formats under an open license.
preferences with the highest confidence score. Unfortunately, there are no KG and Ontologies already
The main intuition behind this model is the possibility available which outline the hierarchies of job professions
to make dialogues as interactive and efficient as possible, and their belonging fields. Accordingly, we have started
allowing the system to negotiate with users whether to implement a new LOD resource that perfectly fits the
results do not completely match their preferences. GUapp intents, besides being also available for other re-
To be updated with all new jobs that the market offers, lated purposes. To the best of our knowledge, the GUapp
G U a p p implements a Crawler that daily extracts the job KG identifies the first attempt of structuring relations be-
positions from Gazzetta Ufficiale, the official journal of tween professions and their application fields under the
record of the Italian government. It directly communi- guidelines of the LOD protocols. Moreover, it integrates
cates with the Orchestrator for storing all the obtained also all the data related to the job recommending task
data into the Elasticsearch instance as new documents. obtained from other state-of-the-art solutions, like cities,
Finally, we have provided to the system a domain- regions, and countries provided by Dbpedia.
specific Knowledge Graph built upon several sources, like We first collected all the RDF statements about loca-
Dbpedia and the ISTAT website, as stated before. The tions from the Dbpedia project and the associated on-
latter identifies a collection of raw data that are not in tology to create a KG that was complete and consistent
KG form. As a consequence, we have modelled a new for this work. For this purpose, we have exploited the
Ontology on which the overall KG can rely. It defines OpenLink Virtuoso6 , a Dbpedia SPARQL endpoint that al-
relations and hierarchies about professions, domain of lowed us to perform different SPARQL queries to collect
competences, locations and so forth and it wisely inte- the interested data. Regarding professions and appli-
grates the raw information with the already structured cation fields, we opted to create a new ontology from
ones. This allows G U a p p to be highly knowledgeable about scratch, assembling this information from highly reliable
5 6
https://www.elasticsearch.com https://dbpedia.org/sparql/
sources. We found that the ISTAT website, managed by knowledge base and the ontology are used in order to
an Italian research institute for statistics, totally accom-
handle the negotiation. Given the conversational nature
plish this requirement. It stored a complete hierarchy of
of the system, a preliminary step for preference elicitation
professions organized for sectors, application fields, and
is needed. In this phase, the agent asks the user some rel-
services in a tree data structure navigable through web evant information about her preferences. In particular, it
pages for each position. For example, at the higher level,
asks about the geographical area and the field of interest.
we can found distinctions between intellectual, technical,
This first conversation phase follows a well-defined dia-
and office jobs while descending into the graph groups logue flow. Let us consider a scenario in which Claudio, a
like scientific, health, and managing positions are out-user who is graduated as Computer Engineer, is looking
lined. This taxonomy reflects what the GUapp ontology for jobs in the computer engineering field. Therefore, he
asserts about professions. The leaves of the ISTAT tree visits the GUapp website and finds the section related
describe all the positions currently recognized in our so-
to the conversational agent. After a standard welcome
ciety, like computer scientists and computer engineers, message, the agent asks for the geographical area Clau-
which compose some of the GUapp KG facts. dio is interested in. In this case, Claudio writes that he is
All this data are automatically retrieved from the previ-
interested to work in Rome. The second crucial question
ously mentioned website exploiting the Crawler routines.that the agent asks to Claudio is about the job position
They not only collect all the job calls of the day, but they
he would like to cover. He answers stating that he is
also scrape all the information that populates the KG. interested in a job as Computer Engineer. At this point,
Then, an owl file is generated for the GUapp ontology the agent tries to map Claudio’s responses to entities and
and all the RDF triples are realized to form the KG. Forclass in the KG. In this particular case, the agent under-
instance, the Computer Engineer profile belongs to the stands that Claudio is interested in jobs proposal in Rome
GUapp class Electronic Engineer, a subclass of Engineer-(that is linked to the entity gpr:roma) regarding a position
ing and of the higher Intellectual and highly specialized
of computer engineer (which refers to the ontological
Scientific profession class. These facts are finally inte-
class gpo:computerEngineer). As a result, by exploiting
grated with those obtained from Dbpedia and form the the Elasticsearch index, the agent starts searching for
overall GUapp KG. At the current state, our KG is lim- jobs that match the Claudio’s requirements and creates
ited to the Italian language and it has data restricted to
a list of possible recommendations that perfectly match
professions, fields, and locations. Nevertheless, thankshis preferences. The recommendations are ranked based
to its semantic structure, we plan to expand it by adding
on the relevance score described in section 3. Therefore,
several languages besides including other job position the agent provides only the first job proposals in the
features like user skills and job goals. ranked list in order to not impact negatively the user ex-
Our system relies on this knowledge source mainly forperience. If Claudio asks for more results, the agent will
implementing two functionalities. The first one is the explore the ranked list in order to provide other possi-
labelling phase of the crawled dataset that makes the rec-
ble recommendations until he is satisfied or no more job
ommendation possible. In detail, the job calls retrievedproposal is available. If Claudio found an interesting job
by the Crawler are in the form of unstructured text, so call, the interaction ends, otherwise the scenario is more
we found it necessary to index the documents on a searchinteresting since the agent is not able to provide other
engine like Elasticsearch. Looking for the KG labels in solutions that perfectly match the user’s needs. In that
each job call, we enriched all the collected texts with the
case, the agent needs more information to understand if
GUapp linked entities, which is helpful in performing the user is willing to travel or how flexible is with respect
recommendations given the users’ preferences. Instead, to the job place. We refer to this phase as negotiation.
the second functionality allows the system to perform Figure 2 shows an example of the behavior of the agent
the preference elicitation and the negotiation steps dur-
in the negotiation and how it exploits the ontology for
ing a conversation. Lead by the GUapp KG entities and predicting other possible recommendations. At the end
categories, our conversational agent realizes dialogues of the preference elicitation phase shown in the previous
deeply related to the recommendation domain. It also example, the agent creates a ranked list of possible job
grants to manage two highly felt issues in the RecSys recommendations in the computer engineering field in
community, like the cold start problem and the absence Rome. Thus it provides to Claudio the first result that
of items to suggest. is job offer as a researcher offered by the University La
Sapienza that is an instance of the Computer Engineering
5. Recommending Jobs through class in the ontology. Let us assume that Claudio does not
Dialogues find the job proposal interesting and he asks for more re-
sults. For the sake of simplicity, we assume that no more
In this section, we present an example of the ontology- alternatives are available. In order to provide other solu-
driven conversation with some details about how the tions, the agent needs more information about Claudio.
Figure 2: An example of Ontology driven negotiation. The messages in blue refer to the recommendations provided by the
agent. The message in orange is the one that triggers the backward process in the ontology. Blue nodes in the ontology are
the classes associated to the job proposals, while orange nodes are those explored in the negotiation phase.
In particular, since there could be possible job offers in want the system to search for similar jobs or areas of
slightly different fields, it asks about the user’s flexibility work. In this case, the agent will ask if the user is willing
with respect to the job place. Following the example, the to travel and, according to the answer, it will provide job
agent asks Claudio ”I can recommend you similar jobs in proposals more or less distant from the original request.
slightly different fields. How are you flexible in this sense?”. All the information about geographical entities is orga-
The agent maps the Claudio’s answer to the number of nized into the knowledge base. This allows the system to
edges that it could navigate backward in the ontology. navigate the graph and find possible job offers that are
For instance, if Claudio answers ”Not so much”, it means in the same geographical area.
that he is not interested in jobs that differ too much from
the field he proposed previously. For this reason, the 6. Conclusion and Future Work
answer is mapped to a maximum of 2 backward hops in
In this paper we presented G U a p p , a platform for search-
the ontology. In case Claudio replies with ”Quite flexible”,
ing jobs in the Italian public administration. We have
the agent maps the answer to a number of 3 backward
outlined the architecture designed to make possible the
hops. This mapping has been empirically defined. Since
job recommendation task in a conversational setting, be-
our ontology is composed of 6 levels, 2 backward hops
sides describing the overall structure of the G U a p p KG and
allow the system to provide job recommendations that
its ontology. Thanks to that, G U a p p consists of a recom-
belong to a similar domain as the previous ones. In this
mender system that suggests relevant jobs to the users,
sense, the agents navigates backward the ontology start-
and one of the most interesting aspects is the integration
ing from the class gpo:computerEngineer and reaching the
of a KG that helps driving the dialogue, making the inter-
parent node gpo:electronicEngineeringAndTLC. Starting
action more natural and pushed at a finer grained level.
from the inner node reached after the backward phase, a
Indeed, the preference elicitation becomes incremental,
forward step is needed to reach the leaves of the sub-tree.
with the possibility of refining and improving the user
At this point, following the example, the agent reaches
requests. We are planning of introducing other several
the leaf gpo:electricalEngineer that, for simplicity, is the
fine-grained features for making the ecosystem competi-
only node in the current sub-tree associated to possible
tive with other state-of-the-art solutions, testing the User
job offers in Rome. Also in this case, it creates a ranked
Experience with an A/B test and setting up a platform in
list following the same approach described previously
order to share the dataset coming from G U a p p usage with
and provides the user the most relevant alternatives.
the industrial and academic community, with the respect
The same method described in this section can be used
of the privacy concerns.
in another possible scenario. Assuming that, during the
negotiation phase, the user answers that she does not
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