“eRReBIS” Business Intelligence based Intelligent Recommender System

for e-Recruitment Process

Siwar Ayadi

, Manel Bensassi

and Henda Ben Ghezala

RIADI Lab, National School of Computer Science, Manouba University, Tunisia

Keywords:

e-Recruitment, Business Intelligence, Content-based Recommendation, Similarity Measure, Prescriptive

Analysis, Machine Learning Algorithms.

Abstract:

Due to the continuous and growing spread of the corona virus worldwide, it is important, especially in the

business era, to develop accurate data driven decision-aided system to support business decision-makers in

processing, managing large amounts of information in the recruitment process. In this context, e-Recruitment

Recommender systems emerged as a decision support systems and aims to help stakeholders in ﬁnding items

that match their preferences. However, existing solutions do not afford the recruiter to manage the whole

process from different points of view. Thus, the main goal of this paper is to build an accurate and generic data

driven system based on Business intelligence architecture. The strengths of our proposal lie in the fact that it

allows decision makers to (1) consider multiple and heterogeneous data sources, access and manage data in

order to generate strategic reports and recommendations at all times (2) combine many similarity’s measure in

the recommendation process (3) apply prescriptive analysis and machine learning algorithms to offer adapted

and efﬁcient recommendations.

1 INTRODUCTION

E-recruitment platforms, as decision support system,

has increasingly been used in the industry particu-

larly with the epidemic widespread and accomplished

clear advantages for both recruiters and job-seekers

by reducing the recruitment time and advertisement

cost. Data has been largely widespread in the job

market.Consequently, these platforms suffer from an

inappropriateness of traditional information retrieval

and exploitation techniques (Al-Otaibi and Ykhlef,

2012). Recommendations for e-recruitment systems

generally differ from recommendations generated in

other contexts (e.g. movies, e-commerce), given that

the job seekers’level of competencies and knowledge

rather then their interests is key for suggesting the

most appropriate position. In fact, the problem of

matching jobs and candidates can be enhanced from

two distinct perspectives: (a) ﬁnd relevant candidates

to a job opening; and (b) select the suitable jobs to a

speciﬁc candidate.

Thus, the challenge of making recommendations

and to develop an accurate decision support system

https://orcid.org/0000-0002-6085-4986

https://orcid.org/0000-0002-0224-6165

https://orcid.org/0000-0002-6874-1388

is closely tied to which and how data are extracted,

transformed and conveyed. Over the past few years,

Jobs’ recommendation became an important task for

the modern recruitment process in order to improve

manager experience.

On the other hand, existing recommender solu-

tions do not afford the recruiter to manage the whole

process. Recruiters need to capitalize on clear KPIs

inside of Talent Management through guided and dy-

namic insights. Consequently, Explanation for rec-

ommendation systems through the deployment of a

KPIs dashboard with the purpose of helping the re-

cruitment’s team make more precise decisions is un-

derlined.

In order to support this research area, we describe,

in this article, a Content based Recommender which

relies on a Business Intelligence architecture, devel-

oped in a computer science company, part of a larger

group, whose objective was to improve the manage-

ment of e-recruitment process searching for more ad-

equate means to attract qualiﬁed coworkers. Our pro-

posal is based on prescriptive analytics that assists de-

cision makers in identifying data-driven strategic de-

cisions.

This article is organized as follows. We ana-

lyze the e-recruiting business requirements and re-

Ayadi, S., Bensassi, M. and Ben Ghezala, H.

“eRReBIS” Business Intelligence based Intelligent Recommender System for e-Recruitment Process.

DOI: 10.5220/0011530200003318

In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 373-380

ISBN: 978-989-758-613-2; ISSN: 2184-3252

373

view related recommender systems proposals for e-

recruitment management in section 2. eRReBis Rec-

ommender System overview and architecture are pre-

sented in Section 3. Usage scenario and the inter-

pretation of results are discussed in Section 4. The

evaluation of efﬁciency of the proposal system are de-

scribed in section 5. Finally, conclusions are drawn

and future works are suggested in Section 5.

2 RECOMMENDATION SYSTEM

IN E-RECRUITMENT:

BUSINESS REQUIREMENT

AND RELATED WORKS

In recent years, data are overwhelming, growing in

size and in connections, creating a potential chal-

lenge of information retrieval in a complex environ-

ment. Filtering, extracting, and prioritizing relevant

information is a pressed requirement for data ‘s users

and analytics, especially, decision makers in the busi-

ness management ﬁeld. In this context, recommender

systems have been developed to search into a large

amount of data and select relevant information ac-

cording to user’s preferences, interest, or observed be-

havior about item (Karimi et al., 2018)

In order to assist the recruiter in looking for

new talents and decision-maker in deﬁning strate-

gic guidelines for the e-recruitment process, recom-

mendation system, as decision support system (Stohr

and Viswanathan, 1998), afford a ﬂexible support

to different stakeholders. In literature, many rec-

ommender system have been proposed. Casagrande

(Casagrande et al., 2017) proposes person-job recom-

mendation system using the proﬁle information from

both candidates and jobs descriptions in order to ﬁnd

a good match between talents and jobs. Ramanath

(Ramanath et al., 2018) explored machine learning

of candidate potentials to determine a ranked list of

the most relevant candidates from the LinkedIn thou-

sands of candidates. Recently, Norbert (Jiechieu and

Tsopze, 2021) followed the ideas of recommender

systems and proposed a comprehensive job recom-

mender system based on competency prediction and

resume classiﬁcation. He used classiﬁcation model

based on neural networks in order to predict high level

skills imprecisely written.

Other works are dedicated to semantic modeling.

For example, Paquette (Paquette, 2014) proposes a se-

mantic modeling of skills which allows to model dif-

ferent level of granularity of skills. Zhao et al. (Zhao

et al., 2015) proposed a combination of Named En-

tity Recognition and Named Entity Normalization to

identify skills from texts, considering them as named

entities. Cheng et al. collected the job-related infor-

mation from various social media sources and con-

structed an inter-company job-hopping network to

demonstrate the ﬂow of talents. (Wang et al., 2013)

predicted the job transition of employees by exploit-

ing their career path data. Xu et al. (Xu et al., 2016)

proposed a talent circle detection model based on a

job transition network which can help the organiza-

tions to ﬁnd the right talents and deliver career sug-

gestions for job seekers to locate suitable jobs.

However, these systems are not enough sophisti-

cated and presented some limits related to the man-

agement of e-recruitment process. In fact, enterprises

require better management of the data and systems

involved in decision-making. To do so, we conclude

that decision-makers need to:

• Deploy an agile IT architecture that can integrate

an increasing number of data sources required for

decision-making, as well as external and big data

sources.

• Increase beneﬁt from collective intelligence in de-

cision making process by exploring collaboration

and participation in data analysis and planning

processes to improve performance and to eas-

ily move from strategic to operational point of

view (and reverse) to a more democratic style of

decision-making.

• Deﬁne, pervasively use and visualize KPIs across

the organization through dashboard to achieve a

common foundation for decisions, align measures

of success and focus data governance on impor-

tant data.

From this point of view, presented works still in-

sufﬁcient and do not cover the whole business require-

ment to manage the e-recruitment process. Compa-

nies had to embrace ﬂexible business structures and

to change decision-making cultures from decisions

based on intuition and experience to data-driven

decision-making in order to identify new business

opportunities, predict future trends and behavior.

On the other side, many online recruiting plat-

forms suffer from an inappropriateness of Boolean

search methods for matching applicants with job re-

quirements. Consequently, a large number of candi-

dates missed the opportunity of recruiting (Lang et al.,

2011). Actual practices and theoretical thoughts show

that this search type is insufﬁcient for achieving a

good ﬁt between candidate aptitudes and job require-

ments (F

arber et al., 2003).

Further, given the business requirements, re-

cruiters need to capitalize on clear KPIs inside of

Talent Management through guided and dynamic in-

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

374

sights. Consequently, the deployment of a KPIs dash-

board with the purpose of helping the recruitment’s

team make more precise decisions is underlined.

3 BI BASED INTELLIGENT

RECOMMENDER SYSTEM

FOR E-RECRUITMENT

PROCESS MANAGEMENT

DESIGN

3.1 Motivations

Our main goal is to provide a theoretical model along

with a proof-of-concept implementation that:

• could be adopted and appropriately adapted to en-

hance the e-recruitment management from back to

front ofﬁce.

• assists decision makers in identifying data-driven

strategic decision and help them to avoid the lim-

itation of standard data analytics.

Thus, the design of our approach is dedicated to the

need to exploit capabilities of BI paradigms towards

the enhancement of data driven decision making in

the e-recruitment management process. The blending

of BI tools, machine learning algorithms and recom-

mender system is considered promising for the im-

provement of data driven decision making in the e-

recruitment ﬁeld and for the development of solutions

able to provide effective and adapted recommenda-

tions by combining different techniques of similarity

measures.

3.2 eRReBIS: Conceptual Architecture

In the proposed approach (Fig 1), heterogeneous data

are collected transformed according the following

process

Step 1: Data Gathering: from multiple data sources

that are structured by Company’s developers. We con-

sider:

• Byblos data source that contains Company’s em-

ployee information.

• Application Tracking System (ATS) Database

from which passive candidate proﬁles looking for

a new opportunity are created.

• LinkedIn from which relevant Company’s job data

is extracted. To do so, we have opted for the web

scraping (screen scraping) method, based on au-

tomatic data retrieval implemented in python.

Step 2: Data Integration: is ensured by the ETL

(Extract, Transform, Load) data integration process,

which takes care of collecting all the necessary infor-

mation from the various sources.

Data Pre-processing and Cleaning: Heterogeneous

data requires a homogenization and integration pro-

cess. The ﬁrst stage consists of eliminating the ir-

relevant data that represents a noise. In the second

stage, we removed duplicated data, punctuation and

numbers. The tokenization stage is an essential phase

in order to remove stop words and irrelevant words.

In this step,we also integrate an algorithm to measure

the similarity between the descriptions of the offers

and the proﬁles of the candidates. Transformed and

adapted data to the feeding phase is stored in a spe-

cialized database “data Mart” which is integrated in

the ”Data Warehouse”.

Step 3: Data Analysis: is the graphical represen-

tation of the performance indicators and recommen-

dations in a way that is understandable and helps the

decision maker to make strategic and relevant deci-

sions. For this end, we analyse data with the Tabular

Model which is based on a new in-memory engine for

tables. The in-memory engine loads all table data into

memory and answers questions directly from mem-

ory. This is very fast in terms of response time. Un-

like the multidimensional model, the tabular model

processes the data warehouse in a single processing

block without dividing it into cubes.

Step 4: Data Visualization: whose objective is to

make access to the decision much more immediate

through graphic representations and indicators map-

ping.

Step 5: Prediction and Recommendations: Our

”eRReBIS” system provides predictive analytics and

guided recommendations to decision makers in order

to provide additional insights. Given the fact that our

training data is labeled and has output variables cor-

responding to the the input variables, supervised ma-

chine learning algorithm was the most suitable to our

business requirement. In addition, our main goal is

to support decision-maker tp predict the evolution of

strategic KPI: the number of incoming employees, the

number of resignations and hires,...Then, we need a

regression algorithm to build a mathematical model

from the training data. Then for this study, to choose

the most suitable regression model to the extracted

data, a comparative and evaluation study is elaborated

between these two supervised and regression algo-

rithms:

• Random Forest Regression: is a supervised

learning algorithm that combines the prediction

of several automatic learning algorithms to make

a more accurate prediction than a single model.

“eRReBIS” Business Intelligence based Intelligent Recommender System for e-Recruitment Process

375

Figure 1: Conceptual Architecture.

During the learning process the algorithm builds

decision trees that work in parallel.

• Linear Regression: is a supervised prediction al-

gorithm, its objective is to predict from the de-

pendent variables of the independent variables by

ﬁnding a function of linear prediction y=f(x) in

order to predict values which are not observed.

There are two types of simple and multiple lin-

ear regression, according to the number of depen-

dent variables. In our case we have two dependent

variables (month, year) so it is the multiple linear

regression

Finally, Data is represented in an e-recruitment dash-

board that aims to make access to the decision much

more immediate through graphic representations and

indicators mapping presented in the following sec-

tion.

3.3 e-Recruitment Dashboarding based

on KPI Cartography

To build such a dashboard, the ﬁrst step is to under-

stand with greater detail the real pains of Talent Man-

agement and the main opportunities that could be cre-

ated for the short and long term by answering these

questions: What is currently not possible to analyze?

Which analyses and information would be necessary

for a decision to be made comfortably? For each

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

376

analysis, which metrics would help take the actions

chosen above? The recruitment process is divided

into two phases: pre and post hire. In order to re-

spond to manage the whole process chronology, we

organise the e-recruitment KPI into:

• Pre-hire Indicators (calculated before the re-

cruitment): The ﬁrst pre-hire indicators are spe-

ciﬁc to the application tracking system (SmartRe-

cruters) as the recruitment process begins online.

Then, pre-hire indicators are automatically calcu-

lated as soon as the recruiter receives the applica-

tions.

• Post-hire Indicators (calculated after the recruit-

ment): The second post-hire indicators are calcu-

lated from the company’s ”Byblos” database af-

ter hiring by using the dimensions ”Personnel”,

”Contract” and their attributes.

In order to assist different stakeholders in decision

making process at different levels (operational, ana-

lytical and strategic), we propose, a cartography of

indicators organised as follow:

• Operational Indicators: which show data re-

lated to daily e-recruitment operations. The main

purpose of an “operational dashboard” is to pro-

vide a comprehensive snapshot of e-recruitment

performance. For example, we cite : Number

of applications that represents the total number

of applications received by the recruitment chan-

nels, Hiring rate, Number of inactive or active

employees.

• Analytical Indicators: such as Expected num-

ber of hires and expected number of resignations,

which use historical company data to identify

trends that can inﬂuence future decision-making.

The ideal audience for viewing analytical dash-

boards are database analysts, as they typically re-

quire a level of understanding that a typical busi-

ness user may not possess.

• Strategic Indicators: which offers a decision-

maker to track performance in relation to strategic

key performance indicators to better align actions

with strategy such as cooptation indicator which

measures ”participative recruitment” (number of

applications from internal source). In fact, Coop-

tation is a recruitment method that enhances col-

laboration and employee participation in the re-

cruitment process by recommending talent for a

speciﬁc position.

3.4 e-Recruitment Recommender

Functionalities

Figure 2 shows the use case breakdown of the eRRe-

BIS System. The roles involved in the process include

both human actors and machine system components

(shown, respectively, as man icons and gray rectan-

gles in Figure 2 ). The recruiter has the possibility to

conﬁgure the dashboard, to select the axis of analysis

and to explore operational and tactical KPI through

descriptive analysis.The process of descriptive anal-

ysis is split into job seeker reporting (visualising of-

fers list) and recruiter reporting (visualizing candidate

proﬁle). Recruiter reporting is the process in which

similarity computed and all aspects related to job po-

sition and each candidate proﬁle are visualized . The

recruiter deals with lists of offers, possibly organiz-

ing them hierarchically in sub groups through recom-

mendation engines. Then, he could visualize the most

appropriate proﬁle to a job position and conversely.

The role of the decision-maker is to deal with the

strategic dashboard through predictive analysis when

necessary to deﬁne the recruitment requirements and

strategies.

eRReBIS System is also based on the “Recom-

mendation engine” that is an autonomous module in

charge of executing the recommendation algorithm

and computing the similarity measure.It is based on

predictive analysis which is a process during execu-

tion and it always follows descriptive analysis.

Recommendation engine ensures prescriptive

analysis which is the process by which the job seeker,

recruiter and decision-maker ﬁnally makes decisions

about the most suitable position, the most suitable

proﬁle or about recruitment strategies. Based on the

output of predictive analysis and the computation of

the similarity’s measure, recommendations are pro-

posed to the job seeker, recruiter and decision-maker

by eRReBIS solution. To do so, we propose a content

based recommendation system and algorithm which:

• Creates vectors from jobs’ databases and proﬁles.

• Combines semantic and syntactic similarity mea-

sures to reconcile documents represented in vec-

tor form.

A comparative study has been conducted, to show

the efﬁciency of our measure compared with classi-

cal measures, is presented in section 5.0.2.

eRReBIS system was developed by combining

several tools and languages. In fact, we visualize

integrated data using the Power BI tool. Recom-

mender Engine, coded with python language, was in-

jected into the integration process deployed with Tal-

end Data Integration (see ﬁgure 2).

“eRReBIS” Business Intelligence based Intelligent Recommender System for e-Recruitment Process

377

Figure 2: eRReBis Functionnalities.

4 CASE STUDY AND RESULT

INTERPRETATION

Our prototype provides several views supporting ﬁl-

tering in the result set and integrates:

• KPI cartography for strategic, analytical and oper-

ational decisions based on straightforward charts

to interpret.

• Prescriptive analysis based on predictive machine

learning algorithm and adapted recommendations

Building upon these features, we divide our dash-

board into 3 views to support the recommendation

process:

• Strategic View: (ﬁgures 3 and 4) Main KPIs of

Talent Management, not with the objective of a

micro analysis but a general view of how the met-

rics are doing, being able to identify any potential

issues. KPIs such as Forecast, hired, candidate to

hire, and others. This can all be ﬁltered by job or

area.

• Analytical View: also known as tactical view,

shows a more speciﬁc view for the leader of the

Figure 3: Analysis of applications.

Figure 4: Turnover analysis.

area (recruiter). Has some analyzes which are

more broken up by date for example.

A snapshot of the dashboard illustrates generic re-

sults on the offers and proﬁles of the candidates,

such as the total number of offers, number of of-

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

378

fers by location, as well as the total number of

proﬁles of the candidates and the proﬁles by se-

niority

• Operational View: This view is designed to be

used by recruiters and offer a very micro view of

how each job is doing, the pipeline, forecast etc.

This is all ﬁltered by the recruiter.

• Multi-varied Recommendations on Different

Activities and Steps (ﬁgures 5, and 6): The re-

cruiter has the possibility to choose an offer from

the list extracted from the linked In. He can visu-

alize a table that contains the proﬁles with the sim-

ilarity measure. The most suitable proﬁle with the

highest similarity measure to his offer is shown in

ﬁgure 5. The selected proﬁle could be more ad-

equate to another position proposed by the com-

pany. eRReBIS prototype offers the possibility to

the recruiter to evaluate the adequation between

this proﬁle and all offered positions. He could se-

lect, as shown in the ﬁgure, the same proﬁle re-

sulting from the ﬁrst search and visualize the sim-

ilarity measure with all the offers of the company.

In this way, he can visualize the most adequate of-

fer for this proﬁle. Comparing scores, the last one

is more appropriate.

Figure 5: Analysis of the offer recommendations.

Figure 6: Analysis of Proﬁle Recommendations.

5 EFFICIENCY EVALUATION OF

eRReBIS

5.0.1 Prediction Algorithm Performance

For the evaluation process of the prediction algorithm,

we used two measures : The term R score 1 and The

Root mean square error (RMSE) 2

∑

− y

)

∑

−

−→

)

(1)

RMSE =

∑

− y

)

(2)

The result of the comparative study is shown in the

ﬁgure 7 and 8. The algorithm “Random Forest regres-

sion algorithm” that has the lowest value of RMSE

and the highest value of R score was adopted.

Figure 7: R2 Results.

Figure 8: RMSE Results.

5.0.2 Evaluation of Similarity Measures

Combination

The job seeker data is taken from Babylos and ATS

database. The data set contains 24734 candidates

plus 7270 active collaborators . In total we have

32004proﬁles, where each proﬁle contains weighted

competencies and 70 job positions. For testing pur-

poses, we divide the user ratings dataset into training

(80%) and testing sets (20%). We perform a 4-fold

cross validation and results are averaged over the 4

cycles of execution. We conducted a set of experi-

ments using four different scenarios. Scenario 1 uses

the Content based Filtering method only. We con-

ducted three sub experiments with different syntac-

tic measures(Pearson, Cosine and Jaccard).Scenario

“eRReBIS” Business Intelligence based Intelligent Recommender System for e-Recruitment Process

379

2 uses the semantic similarity method only, Scenario

3 uses a hybrid of syntactic and semantic similar-

ities methods with equal contribution (both have a

weight of 0.5).We present the evaluation of the pro-

posed similarity measure compared with the other

measure. Similarity measures are computed as shown

in 1. When we combine cosine and semantic similar-

ity, we obtain optimal results and it is more accurate

than a single measurement. We evaluate recommen-

dation accuracy by using Precision as shown in table

Table 1: Similarity measurement results.

Syntactic Similarity Semantic

Similarity

Hybrid Simi-

larity

Proﬁle

Job ID Pearson Cosine Jaccard Spacy

NLP

Cosine and

Spacy NLP

1 1 0.34 0.44 0.41 0.51 0.47

2 2 0.45 0.38 0.21 0.32 0.35

3 3 0.74 0.92 0.67 0.99 0.9

4 4 0.56 0.58 0.45 0.62 0.6

5 5 0.33 0.41 0.12 0.38 0.35

Table 2: Evaluation recommendation.

Syntactic similarity Semantic

similarity

Hybrid simi-

larity

Jaccard Pearson Cosine Spacy

NLP

Cosine &

Spacy NLP

Total of recommenda-

tions

180 166 208 195 248

Pertinent recommen-

dations

77 68 92 84 139

Precision Value 0.42 0.4 0.44 0.43 0.56

6 CONCLUSION AND

PERSPECTIVES

“eRReBIS” is data-driven strategic decision system

and differs from the existing approach in that it re-

lies mainly on data management based BI architec-

ture. We propose Dynamic Multiviews dashboard-

ing (strategic, tactical, operational) based on prescrip-

tive analysis in order to assist the recruiter to iden-

tify trends, to create new business opportunities. Our

prototype is a content-based recommendation engines

that deploy a combination of similarity measures in

order to classify the candidates. We show that by

combining the similarity measures between the jobs

and skills, our model provides better recommenda-

tion for both recruiter and candidate. Additionally,

we also show some case studies which validate our

claims. Our system is scalable and can be gradually

enhanced by other massive data sources from social

networks and Business platforms. Furthermore, the

system architecture can be improved by other intel-

ligent features and components of deep learning al-

lowing the inference of strategic recommendations.

Future research will focus more on deep and predic-

tion analysis with machine learning techniques to im-

prove the performance of recommenders and linked

data proﬁling in the knowledge representation.

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