Content-based Filtering for Worklist Reordering to improve User
Satisfaction: A Position Paper
Sebastian Petter
, Myriel Fichtner
, Stefan Sch
and Stefan Jablonski
University of Bayreuth, Germany
University of Regensburg, Germany
Business Process Management, Recommender Systems, Content-based Filtering, User-centered Process
Improvement, User Satisfaction, Worklist Optimization, Process Activity Similarity.
Business Process Management (BPM) is an approach to optimize business processes regarding certain com-
pany goals, e.g., the duration or quality of process outcome. Human resources are essential to business pro-
cesses and are often neglected during process optimization. Considering domains focusing on users, it can be
observed that recommender systems are often used to support user decisions and increase user satisfaction.
This inspired us to use recommendation techniques in the context of BPM. Employee satisfaction significantly
influences productivity, while employees are more satisfied when their preferences are taken into account dur-
ing process execution. In this work, we propose to adopt the concept of content-based filtering to recommend
worklist items to process participants they probably prefer. Since this work is part of a research project, we
illustrate our approach on a simplified real-world business process from one of our application partners.
Business processes can be understood as a series of
activities to be completed to achieve a company goal.
Optimizing such business processes is one of the pri-
mary purposes in research related to Business Process
Management (BPM). Usual goals are to shorten pro-
cessing time or to increase the quality of process out-
comes (Jablonski and Bussler, 1996), (Koulopolous,
1995), (Lawrence, 1997). To reach optimization,
companies need to gain insight into the structure of
their processes. An established method is to visual-
ize them using process modeling languages such as
the Business Process Model and Notation (BPMN)
(OMG, 2011). The resulting business process model
contains the sequence of required steps and process
entities involved, like process participants or data ob-
The project is financed with funding provided
by the Federal Ministry of Education and Research
and the European Social Fund under the ”Future
of work” programme and managed by the Project
Management Agency Karlsruhe (PTKA). The author
is responsible for the content of this publication.
jects. Process-Aware Information Systems (PAIS) are
used to manage and execute operational processes
based on process models. Every executed activity
is recorded in a so-called process event log during
process execution. In addition, further details are in-
cluded, like execution durations or resources execut-
ing activities (van der Aalst, 2016). Process mining
techniques allow process optimization through the ex-
traction of knowledge from event logs (Marin-Castro
and Tello-Leal, 2021). Most approaches are activity-
centered and focus on process control flow (Scho-
nenberg et al., 2008), (Haisjackl and Weber, 2010).
In contrast, many processes executed in small and
medium enterprises are human-driven. Since human
resources are an essential component of business pro-
cess executions in companies, they have to be consid-
ered as a target for optimization.
In times of a shortage of skilled employees, com-
panies have to distinctly focus on the needs and ex-
pectations of human workers. Consideration of such
aspects increases employee satisfaction and decreases
fluctuation. Furthermore, happiness and satisfaction
with assigned work positively impact the employees’
productivity. It is proved that ”happy” workers are
13 % more productive (Bryson et al., 2015) (Bellet
et al., 2019). Furthermore, human resources that have
some degree of control over their work are more pro-
Petter, S., Fichtner, M., Schönig, S. and Jablonski, S.
Content-based Filtering for Worklist Reordering to improve User Satisfaction: A Position Paper.
DOI: 10.5220/0011092900003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 589-596
ISBN: 978-989-758-569-2; ISSN: 2184-4992
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
ductive and more motivated (Russell et al., 2016).
One way of increasing satisfaction and motivation
of employees is to regard their preferences. Discover-
ing preferences of human process participants can be
adopted from the domain of recommender systems.
Recommender systems are used to recommend items
to users. For example, they are used in online shops to
recommend products the user potentially prefers (Ag-
garwal, 2016). The use of such concepts has proven
to be profitable for companies. These results inspired
us to adopt the methods of recommender systems and
using them in the context of BPM for user-centered
process improvement. To the best of our knowledge
existing research applies worklist reordering and op-
timization only for the purpose of improving key per-
formance indicators. User and employee satisfaction,
however, is neglected so far. In this work, we con-
sider the concept of content-based filtering to recom-
mend activities of a running business process instance
to process participants. For this purpose, we intro-
duce two ideas to enhance process activities and pro-
cess event logs to adopt the concept of content-based
filtering to BPM.
As a simple example, we consider a software
development process. Besides other activities, it
comprises the activities ”frontend development” and
”backend development”. Both must be executed by
software developers. Some of them prefer to use
JAVA to HTML. Due to missing knowledge about that
preference, assigning activities to developers occurs
randomly. The developers have to execute these ac-
tivities independent of their preferences and the pref-
erences of co-workers. This might lead to minor mo-
tivation to do the jobs.
Human resources have different preferences
which may affect their motivation (Novak, 1999).
By applying content-based filtering, we create user-
centered process improvement by reordering the
worklist items based on preferences of human re-
sources. This creates a better working atmosphere,
strengthens employee loyalty, and raises productivity.
Furthermore, reordering the worklist has no negative
impact on process performance (Pflug and Rinderle-
Ma, 2015).
The main research question addresses the issue
whether the approach of content-based filtering from
recommender systems can be applied in BPM to in-
crease user satisfaction. From this, two sub-tasks can
be derived:
1. Which information can be exploited by a recom-
mender system to infer user preferences?
2. How should these results be displayed to the
To realize user-centered process improvement com-
bining BPM and recommender systems, an inter-
disciplinary consortium consisting of three research
partners and five SMEs - three application partners
and two implementation partners - collaborated in a
joint project called PRIME
(Process-based integra-
tion of human expectations in digitalized work envi-
ronments) funded by the Federal Ministry of Educa-
tion and Research and the European Social Fund. In
our work, we present an exemplary real-world pro-
cess of one of our partners to show the relevance of
this topic.
The remainder of the paper is structured as fol-
lows. In Section 2, we present the two main areas con-
sidered in this work: BPM and recommender systems
in general and the content-based filtering method in
particular. Furthermore, we show existing approaches
combining these aspects. Section 3 describes the core
idea of recommending activities to the current user of
a PAIS. We conclude our work and give ideas for fu-
ture work in Section 4.
This work considers the two fields of BPM and rec-
ommender systems to accomplish user-centered pro-
cess improvement by means of worklist optimization.
First, we introduce the two research areas, BPM and
recommender systems. We then present related work
that addresses both topics.
Business Process Management. BPM is used to
increase efficiency and reduce costs of processes exe-
cuted in enterprises of all industries and sizes. BPM
considers modeling, executing, and analyzing busi-
ness processes (Dumas et al., 2018). In order to
execute a business process, first, a business process
model is created. A Business Process Management
System executes the model and displays activities that
have to be executed. Activities that need to be ex-
ecuted are represented in a worklist. Since multi-
ple users with the same roles are usually involved
in the process execution, the worklist can be same
for them. Users can select their preferred activities
from the worklist as long as there are no further re-
strictions. Historical process executions of a business
process are stored in so-called (process) event logs.
Therefore each execution of an activity is recorded as
event. Such events encapsulate all relevant informa-
tion about the execution, e.g., execution timestamp or
the involved resources. Other properties can arbitrar-
ily extend this set of information. Events that refer to
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
the same process instance are temporally ordered by
their timestamp and build so-called ”traces” (van der
Aalst, 2016).
Recommender Systems. Recommender systems
predict results for alternative options based on user
preferences. (Guo et al., 2019). Preference is one
of the fundamental attributes to support user (Gold-
smith and Junker, 2008). The value of recommender
systems has been demonstrated in various application
domains like E-commerce and other user-centric web
applications (Manouselis and Costopoulou, 2007),
(Nguyen and Haddawy, 1998). To calculate pre-
dictions, recommendation algorithms require infor-
mation about users and items that should be rec-
ommended. Four main recommendation methods
have been established, namely collaborative filter-
ing, content-based filtering, knowledge-based filter-
ing, and hybrid recommendation, which differ mainly
in the input information used to calculate recommen-
dations. In our work, we focus on content-based
filtering. Content-based filtering aims to classify
items with specific keywords, learn about user prefer-
ences, look up those keywords, and recommend simi-
lar items. Content-based filtering exploites two types
of input data: (i) user feedback (explicit and implicit)
and (ii) item attributes. User feedback is created while
using, for example, web applications. Consider a
webshop, where users can buy products and rate them
afterwards. Buying a product is considered implicit
user feedback (liking this product), whereas the rating
is explicit feedback. Any product of a webshop has
additional information about itself, so-called ”item at-
tributes”, e.g., a book has an author and a genre. User
preferences can be derived from feedback created by
users. The item attributes are used to compare activ-
ities. Combining item attributes and user feedback,
recommendation algorithms can calculate the proba-
bility of a user liking a specific product depending on
the similarity to other, already rated products. The
goal of content-based filtering methods is to predict
the rating of each item considered using training data
where items are rated by the user. Content-based
filtering follows three main steps: (i) Preprocessing
and Feature Extraction, (ii) Learning User Profiles,
and (iii) Filtering and Recommendation (Aggarwal,
2016). During step (i) features are extracted from the
considered item and are converted into a keyword-
based vector space. In (ii), a user-specific model is
created to predict user interest in items. The con-
struction of this model is based on the history of a
user buying or rating items. (iii) establishes recom-
mendations on items for the user to consider while
creating the model. A drawback of content-based
filtering is the cold-start problem.It describes that it
is impossible to create recommendations if we have
no information about the user (e.g., in case of a new
user). Furthermore, only apparent items are often rec-
ommended because the algorithm recommends items
with similar attributes. In return, new items can be
recommended because they are comparable to exist-
ing items due to the item attributes, and the recom-
mendation is not based on preferences of other users.
Furthermore, item attributes can be derived from the
textual description of an item.
Related Work. Now, we provide an overview of
work that combines the two areas Business Process
Management and recommender systems. Most ap-
proaches consider process improvement through rec-
ommendations, whereas the focus is on optimizing
process results or shortening processing time. In
(Schonenberg et al., 2008) and (Haisjackl and We-
ber, 2010) the authors present an approach creating
recommendations on possible next steps through pre-
diction of a partial case. The recommendations are
based on the current execution trace and considera-
tion of similar process instances. The goal is to as-
sist the user in optimal decision-making. A similar
approach is presented in (van der Aalst et al., 2010).
Based on event logs, recommendations are made on
which activity should be executed next to receive a
certain goal like shortened processing time. To sup-
port users in flexible processes, business process mod-
els are enhanced by estimations about runtime and
availability of resources in (Barba et al., 2012). Based
on this information, an optimized execution plan can
be generated. To reduce risks (e.g., exceeding dead-
lines during process execution), recommendations, on
which activity has to be executed next, are made to
the user in (Conforti et al., 2015). Such recommenda-
tions are also made in (Huber et al., 2015). Consid-
ering the successors, activities are recommended to
the user having the shortest runtime. In (Bidar et al.,
2019) the authors make a suggestion on how to de-
rive user-preferences from event logs. The optimal
allocation of resources at process level is computed
in (Cabanillas et al., 2013). In this work, a priority-
based resource allocation is proposed using the Se-
mantic Ontology of User Preferences (Garc
ıa et al.,
2010). Every time a new activity has to be executed,
it is allocated to the resource with the highest pref-
erence. Reordering worklist items to improve key
performance indicators is considered in (Pichler and
Edre, 2019). Furthermore, (Pflug and Rinderle-Ma,
2015) shows, that reordering the worklist of process
participants does not have negative impact on tempo-
ral parameters like throughput time. Summing up, ex-
Content-based Filtering for Worklist Reordering to improve User Satisfaction: A Position Paper
isting research mainly focuses on recommending next
activities and resource allocation for performance op-
To support the comprehensibility of our approach, we
introduce a running example of a business process
modeled in the modeling language Business Process
Model and Notation (BPMN). The simplified busi-
ness process shown in Figure 1 is part of a real-world
example from one of our application partners. It con-
sists of five activities that must be executed by human
resources with the role Team leader. The first step is
to accept an incoming order the Team leader receives
as an E-mail from a customer. Afterward, the Roster
for the technician must be created in Excel and dis-
patched to the technician by E-mail. During the step
”Preparation of order (ID4)”, the Team leader com-
municates with the customer (getting detailed order
information) and with the technician (send detailed
order information as Excel-file by E-mail). The last
step of this process is the treatment of special requests
the technicians send via E-mail.
As described in Section 2.2, user feedback and
item attributes are used as input information for
content-based filtering. In BPM, we can consider ac-
tivities as items.
Thus, we need to get user feedback related to ac-
tivities and attributes describing activities. Implicit
user feedback is created during process execution and
is stored in an event log. This information is declared
as implicit feedback since it relates to execution pa-
rameters (e.g., execution time) and not to information
directly provided by executors. However, to identify
user preferences, explicit feedback in term of user rat-
ing is still missing. Therefore, we need to introduce
an additional step after executing user tasks: the rat-
ing of an executed activity. In our example, we simply
consider like and dislike as activity ratings. In general,
the ratings can be of any complexity.
Another preprocessing step for applying content-
based methods in BPM is tagging activities. In pro-
cess models, activities have labels interpretable as
textual instructions but do not contain information
about semantics. To get more information about an
activity and to be able to compare activities, we need
to add tags or labels to each activity. Our concept con-
sists of three basic elements: (i) Preprocessing and
Tagging, (ii) Content-based Filtering, and (iii) Work-
list Reordering.
Preprocessing and Tagging. The input data for
preprocessing are business process models, process
descriptions, an event log, and the current state of pro-
cess execution provided by a PAIS instance, e.g., the
current users and their worklists. As process model
the introduced example from above is taken. During
execution of this process model an event log is gen-
erated. Every event log entry must include a rating
of whether the executing process participant likes or
dislikes the activity.
To create recommendations based on content-
based filtering, we also need activity tags containing
semantic information. These tags can be generated
from the process model automatically or manually
by a process expert during modeling. Furthermore,
a process expert can manually adapt these keywords
(adding new or removing irrelevant ones). An advan-
tage of this approach is the possibility of consider-
ing and therefore interpretively connecting activities
of multiple processes. Thus, executions of different
processes by the same user can be combined through
these activity tags. This enables to adopt preferences
for similar activities from other processes.
In our running example, we derive five tags from
the business process model: E-mail, Excel, commu-
nication with customer, communication with techni-
cian, and roster. None of the five activities has all
tags. For example, ”Accept order (ID1)” considers
the activity tags communication with customer and e-
mail. The ”Preparation of order (ID4)” activity con-
siders all tags but roster.
Content-based Filtering. To generate recommen-
dations, we consider training data which is defined as
a set of rated activities A
. These activities are rated
by the process participant the items are recommended
to. Since the event log is enhanced by explicit user
feedback, it contains all information to calculate rec-
ommendations with content-based filtering methods.
Therefore we consider it as training data. Every item
of the event log contains an activity ID, the informa-
tion which user executed what activity, and a set of
activity tags.
Besides the training set A
for content-based fil-
tering, an unrated set of activities A
(worklist) is
considered. As described in Section 2, the goal of
content-based filtering methods is to predict the rating
of each activity in A
using the training data. Both,
and A
, are user-specific sets of activities.
The issue we have to solve is similar to that of
classification. An established technique to classify
data sets is the nearest neighbor classifier (Chomboon
et al., 2015). We will use this classification method in
our running example, while it is interchangeable and
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Figure 1: Example: Technician service management process.
can be replaced by any other classification method.
We define a similarity function based on the near-
est neighbor classifier. Similarity/distance functions
like the Euclidean distance or the Manhattan distance
are used for structured or multidimensional data. In
this work, we use the Euclidean distance to calculate
the distance between two vectors to compare two ac-
tivities. Afterward, we transform the results to a sim-
ilarity measure due to better comparability.
The Euclidean distance is calculated as follows:
d(p, q) =
Where p and q are a pair of vectors representing
activities with n attributes. Furthermore, a similarity
metric is needed to improve comparability of the dis-
sim(p, q) =
1 + d(p, q)
For each activity in A
, the similarity metric to
each activity from A
is calculated to identify the
nearest neighbor. Considering the rating of the near-
est neighbor activity, the rating of the related activity
in A
can be deduced.
In our running example, the training set A
based on the event log and the activity tags created
in the Preprocessing and Tagging step. In Table 1 the
training set A
is displayed. The activities with ID1
ID4 have been executed several times and rated by
the current user. Activity ID5 is not displayed at the
table because the team leader has never executed it
until now. Furthermore, the tags for every activity are
considered. In our example the values 0 and 1 show
whether an attribute is present or not. The vector-
representation of activity ID4 is (1, 1, 1, 1, 0) since it is
related to the tags E-mail, Excel, communication with
customer and communication with technician. The set
of unrated activities A
is the worklist with tasks that
team leaders must execute. We assume that the work-
list of the team leaders contains currently five tasks in
the following order where every task can be executed
independent of others:
1. Create roster (ID2)
2. Dispatch roster (ID3)
3. Receive special request (ID5)
4. Accept order (ID1)
5. Preparation of order (ID4)
List 1: Worklist of team leader (FIFO).
To calculate the similarity between ”Receive spe-
cial request (ID5)” and ”Preparation of order (ID4)”
we have to define p as the activity ”Receive special
request (ID5)” in A
and q as the activity ”Prepa-
ration of order (ID4)” in A
. The two vectors v
(1, 1, 0, 1, 0) and v
= (1, 1, 1, 1, 0) are representations
of the activities. Then d(p, q) = 1 and sim(p, q) = 0.5.
Calculating all values for p of any activity in A
, we
notice that 0.5 is the highest value, whereas 1 means
the activities are similar and 0 means that the activ-
ities are completely different. Considering the user
rating of all ”Preparation of order (ID4)” activities,
we notice that the team leader always likes this ac-
tivity. Thus, the team leader possibly likes ”Receive
special request (ID5)” too. Applying this calculation
to all data considered, we achieve the following pre-
dictions: the first item of the team leader’s worklist
(List 1),”Create roster (ID2)”, is rated as dislike. The
activity has been executed by the team leader four
times. Thus, the similarity metric sim(p, q) equals 1.
Content-based Filtering for Worklist Reordering to improve User Satisfaction: A Position Paper
Table 1: Training data A
of one team leader for running example.
ActivityId E-mail Excel Com. Customer Com. Technician Roster ... Like/Disklike
ID1 1 0 1 0 0 ... DISLIKE
ID2 0 1 0 0 1 ... DISLIKE
ID3 1 0 0 1 1 ... DISLIKE
ID4 1 1 1 1 0 ... LIKE
ID1 1 0 1 0 0 ... DISLIKE
ID2 0 1 0 0 1 ... DISLIKE
ID3 1 0 0 1 1 ... LIKE
ID2 0 1 0 0 1 ... LIKE
ID2 0 1 0 0 1 ... DISLIKE
ID3 1 0 0 1 1 ... LIKE
ID4 1 1 1 1 0 ... LIKE
Three of four times, the user did not like the activ-
ity though the prediction for a feature rating is dis-
like. In the same way, the rating for ”Create roster
(ID2)” is calculated, the rating for ”Dispatch roster
(ID3)”, ”Accept order (ID1)”, and ”Preparation of or-
der (ID4)” can be calculated because the user has al-
ready executed these activities. The only task the user
did not execute yet is ”Receive special request (ID5)”.
To predict this task, we need to calculate the nearest
neighbor by calculating the Euclidean distance and
this similarity metric shown in Formula (1) and (2).
The calculation results in a similarity metric of 0.5
regarding the activity ”Preparation of order (ID4)”.
This activity has been executed twice, and the user
liked it both times, thus with a high probability the
user likes the new activity.
Worklist Reordering. Most PAIS organize a work-
list according to the FIFO principle. Taking the re-
sults from step (ii) (Content-based Filtering), we can
rearrange the worklist and adapt it to the requirements
of a current user. The manipulation of the items only
affects the order of the worklist items. We do not omit
any tasks as well as we do not add any new tasks. The
active user can still decide which activity to execute
While displaying the reordered, customized work-
list, it is required to present the reason for reordering
so the user can reproduce why specific activities are
Taking the results of step (ii) (Content-based
Filtering) and adapting the worklist according to
these recommendations, the following reordered
worklist can be generated:
1. Dispatch roster (ID3)
2. Receive special request (ID5)
3. Preparation of order (ID4)
4. Create roster (ID2)
5. Accept order (ID1)
List 2: Preference-based reordered worklist of team leader.
In this case, the content-based filtering method
recommends executing the tasks ”Dispatch roster
(ID3)”, ”Receive special request (ID5)”, and ”Prepa-
ration of order (ID4)” because these are the tasks the
team leader potentially likes, whereas the last two
tasks in List 2 are not among the user’s favorite tasks.
Perhaps another team leader with opposite prefer-
ences performs the latter tasks, thus increasing that
user’s satisfaction.
The example considers a worklist in first-in-first-
out order. However, some approaches exist to reorder-
ing the worklist due to better process performance or
similar (e.g., (Schonenberg et al., 2008)). These pre-
ordered worklists must be considered too. One pos-
sibility of including user preferences into preordered
worklists is to reorder only tasks with the same prior-
ity, not falsifying previous calculations.
Considering the real-world example, we notice
that the activities ”Receive special request (ID5)” and
”Preparation of order (ID4)” are similar, indeed. One
team leader available during process documentation
rated the activities ID1 - ID4 (Table 1). Activity ID5
has not been rated since we want to predict whether
the team leader likes it or not. Presenting the result
of the calculation, the team leader confirmed that he
likes the activity ”Receive special request (ID5)”.
In this work, we provide a novel approach to increase
user satisfaction during business process execution by
worklist optimization through recommender systems
and illustrate it with a running example. We apply
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
the method of content-based filtering to a given work-
list and the event log and present the result, a work-
list, ordered according to potential user preferences,
to the active user of the PAIS. An advantage of this
approach is that existing data and information like the
process model or the event log can be used to cal-
culate the user’s preferences. Furthermore, activities
of the process model can be enhanced by other tags
for more accurate predictions. We evaluated our work
prototypically with our project partners. The idea
received a lot of positive feedback, and the project
partners confirmed that the presented idea leads to an
improvement in user satisfaction. Initial prototypical
implementations and evaluations showed the success
of the approach. Manual calculations of recommen-
dations provided promising results demonstrating the
need for implementation.
In future work, we will extend our approach by
alternate activity rating approaches as well as more
approaches to generating activity tags. Furthermore,
other methods known from recommender systems can
be considered to tackle drawbacks, like cold-start
problem for new users, or recommendation of appar-
ent items, of content-based filtering method, e.g., col-
laborative filtering or knowledge-based filtering. Fi-
nally, we plan to implement a prototype and to include
a real-world evaluation of our approach.
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ICEIS 2022 - 24th International Conference on Enterprise Information Systems