=Paper=
{{Paper
|id=Vol-2960/paper4
|storemode=property
|title=Constraint-Aware Recommendation of Complex Items (Long paper)
|pdfUrl=https://ceur-ws.org/Vol-2960/paper4.pdf
|volume=Vol-2960
|authors=Mathias Uta,Alexander Felfernig,Denis Helic
|dblpUrl=https://dblp.org/rec/conf/recsys/UtaFH21
}}
==Constraint-Aware Recommendation of Complex Items (Long paper)==
https://ceur-ws.org/Vol-2960/paper4.pdf
Constraint-Aware Recommendation of Complex Items
Mathias Uta1,2 , Alexander Felfernig2 and Denis Helic2
1
Siemens Energy AG, FreyeslebenstraΓe 1, 91058, Erlangen, Germany
2
Graz University of Technology, RechbauerstraΓe 12, 8010, Graz, Austria
Abstract
In contrast to basic items such as movies, books, and songs, configurable items consist of individual subcomponents that
can be combined following a predefined set of constraints. Due to the increasing size and complexity of configurable items
(e.g., cars and software), a simple enumeration of all possible configurations in terms of a product catalog is not possible.
Configuration systems try to identify a solution (configuration) that takes into account both, the preferences of the user and a
set of constraints that defines in which way individual subcomponents are allowed to be combined. Due to time limitations,
cognitive overloads, and missing domain knowledge, configurator users are in many cases not able to completely specify their
preferences with regard to all relevant component properties. As a consequence, recommendation technologies need to be
integrated into configurators that are able to predict the relevance of individual components for the current user. In this paper,
we show how the determination of configurations can be supported by neural network based recommendation. This approach
helps to predict user-relevant item properties using historical interaction data. In this context, we introduce a semantic
regularization approach that helps to take into account configuration constraints within the scope of neural network learning.
Furthermore, we demonstrate the applicability of our approach on the basis of an evaluation in an industrial configuration
scenario (high-voltage switchgear configuration).
Keywords
Recommender systems, Knowledge representation and reasoning, Neural networks,
1. Introduction knowledge representation level, configuration problems
can be defined, for example, as a constraint satisfaction
In contrast to basic items such as books, movies, and problem (CSP) [5] or in terms of a rule-based represen-
songs, configurable items are composed of subcompo- tation [6]. Using CSP representations, possible combi-
nents which must be combined conform to a set of pre- nations of individual components are defined in terms
defined constraints [1]. For reasons of combinatorial of constraints with a strict separation of domain knowl-
explosion, it is in many cases impossible to enumerate edge and problem solving knowledge [1]. In contrast, in
the individual items (configurations) in terms of a prod- rule-based approaches product domain knowledge and
uct catalog. Related example domains are automotive [2], problem solving knowledge are intermingled. In this
software (e.g., configuration of operating systems) [3], paper, we use a rule-based knowledge representation
and telecommunication infrastructures [4]. Due to the which is applied in the reported application domain of
increasing size and complexity of configurable items, it high-voltage switchgear configuration.
becomes important to integrate recommendation algo- Due to the increasing size and complexity of config-
rithms into configuration processes to support users in urable items, recommendation technologies are needed,
component and/or parameter selection. that proactively support underlying choice processes.
Informally, configuration can be regarded as a product There exist a couple of approaches that already support
design activity where the resulting item (also denoted as the recommendation of complex items. First, knowledge-
product or configuration) is composed of elements of a based recommender systems [7] support recommenda-
pre-defined set of basic components/parameters [1]. In tion processes on the basis of a product catalog and de-
this context, the chosen components must be consistent termine recommendations either on the basis of a set
with a given set of constraints that define restrictions of strict selection criteria (constraints) [8] or similarity
regarding the possible component combinations. On the metrics [9]. The ranking of items is often implemented
3rd Edition of Knowledge-aware and Conversational Recommender on the basis of a utility analysis [10] or further evaluation
Systems (KaRS) & 5th Edition of Recommendation in Complex criteria that measure to which extent the preferences of
Environments (ComplexRec) Joint Workshop @ RecSys 2021, the user are satisfied by individual decision alternatives
September 27β1 October 2021, Amsterdam, Netherlands [11, 9, 12]. Importantly, with a few exceptions [13, 14, 15],
Envelope-Open mathias.uta@siemens-energy.com (M. Uta);
the approaches to the handling of user preferences in
aflefern@ist.tugraz.at (A. Felfernig); dhelic@tugraz.at (D. Helic)
GLOBE https://felfernig.ist.tugraz.at/ (A. Felfernig) configuration-related scenarios do not take into account
Orcid 0000-0002-1670-7508 (M. Uta); 0000-0003-0108-3146 the preferences of other users but focus more on different
(A. Felfernig); 0000-0003-0725-7450 (D. Helic) types of decision-theoretic optimizations. An overview
Β© 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0). of existing integration approaches of recommendation
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
technologies into configuration systems is provided a.o. 2. Working Example
in Falkner et al. [13]. Existing integrations focus on a
2-phase process where recommendations of feature set- As a basis for the following discussions on integrating
tings are predetermined and then recommended to the neural network based predictions of user preferences, we
user. In the case of inconsistent recommendations, alter- first introduce the definition of a configuration task (see
native recommendations are calculated repeatedly until Definition 1).
a consistent recommendation can be presented. Definition 1. A configuration task can be defined by a
Compared to existing approaches to the integration tuple (π , π·, π
, π
πΈπ) where π = {π£1 , π£2 , .., π£π } is a set of fi-
of recommendation algorithms with configuration, we nite domain variables, π· = {πππ(π£1 ), πππ(π£2 ), .., πππ(π£π )}
show how to take into account configuration constraints is a set of corresponding domain definitions, and π
=
already in the learning phase and thus minimize the prob- {π1 , π2 , .., ππ } is a set of rules that define how a configura-
ability of inconsistent recommendations to be detected in tion can be derived from a given set of customer require-
the subsequent configuration phase. In this paper, we fol- ments π
πΈπ = {π£πΌ = π£πππΌ , .., π£πΎ = π£πππΎ } where elements of
low the idea of case-based recommendation [16] where π
πΈπ are regarded as variable value assignments.
historical configurations with similar parameter settings A simple example of a configuration task definition
as those already specified by the current user are used as is the following (see Example 1) where πππ represents
a basis for identifying nearest-neighbor configurations. a park distance control feature and π π’ππ represents fuel
In our work, we use such a case-based approach as a consumption in gallons/100miles.
baseline version. This version is then compared with two
Example 1: Configuration Task.
different versions of a feed-forward neural network based
configurator integration. The first version focuses on the β’ π = {π‘π¦ππ, πππ, π π’ππ, π πππππ, 4-π€βπππ, πππππ}
prediction of configuration parameter settings relevant β’ π· = {πππ(π‘π¦ππ) =
for the user. The second version follows the same goal {πππ‘π¦, ππππ, πππππ, π₯ππππ£π}, πππ(πππ) = {π¦ππ , ππ},
but also takes into account the fact that recommenda- πππ(π π’ππ) = {1.7, 2.6, 4.2}, πππ(π πππππ) =
tions should be consistent with the underlying constraint {π¦ππ , ππ}, πππ(4-π€βπππ) = {π¦ππ , ππ}, πππ(πππππ) =
set. To support this goal, we propose a semantic regular- {πππ, πππ’π}}
ization of a feed-forward (multi-class and multi-branch)
β’ π
= {π1 βΆ 4-π€βπππ = π¦ππ β π‘π¦ππ = π₯ππππ£π, π2 βΆ
neural network that is used as a configuration parameter
π πππππ = π¦ππ β π‘π¦ππ β πππ‘π¦, π3 βΆ π π’ππ = 1.7 β
prediction model.
π‘π¦ππ = πππ‘π¦, π4 βΆ π π’ππ = 2.6 β§ π‘π¦ππ = π₯ππππ£π β
The major contributions of this paper are the follow-
π πππ π, π5 βΆ π‘π¦ππ = πππππ β π πππππ = π¦ππ , π6 βΆ
ing. (1) we introduce a semantic regularization approach
π‘π¦ππ = ππππ β πππ = π¦ππ }
specifically useful for integrating case-based recommen-
β’ π
πΈπ = {π‘π¦ππ = πππ‘π¦, πππ = π¦ππ , π π’ππ = 1.7}
dation with rule-based configuration environments, (2)
we compare the predictive quality of the developed ap- Given the definition of a configuration task
proach on the basis of a real-world dataset from a complex (π , π·, π
, π
πΈπ), we are able to introduce the definition
industrial configuration task (high-voltage switchgear of a corresponding configuration (solution for a
configuration) with regard to the evaluation criteria of configuration task) β see Definition 2.
prediction quality and recommendation consistency, and Definition 2. A configuration for a given configuration
(3) we show how the presented results can be further task definition (π , π·, π
, π
πΈπ) is a set of variable assign-
generalized to be applicable for configuration scenarios ments πΆππ πΉ = {π£1 = π£ππ1 , .., π£π = π£πππ } where β{π£π = π£πππ }
beyond rule-based configuration. β πΆππ πΉ βΆ π£πππ β πππ(π£π ) and ππππ ππ π‘πππ‘(πΆππ πΉ βͺπ
βͺπ
πΈπ).
The remainder of this paper is organized as follows. A configuration is complete if each variable in π has an
In Section 2, we introduce a working example in terms assignment in πΆππ πΉ.
of a simplified configuration knowledge base from the
automotive domain. In this context, we also introduce An example configuration πΆππ πΉ for the configuration
the concepts of a configuration task and a corresponding task of Example 1 is the following (see Example 2).
configuration. Thereafter, in Section 3, we introduce our Example 2. πΆππ πΉ = {π‘π¦ππ = πππ‘π¦, πππ = π¦ππ , π π’ππ =
neural network based approach to the recommendation 1.7, π πππππ = ππ, 4-π€βπππ = ππ, πππππ = πππ}
of configuration parameter settings. In Section 4, we sum- We regard a configuration as complete if each of the
marize our evaluation approach and report the results variables in π is associated with a corresponding value
of an evaluation conducted on the basis of a real-world assignment and these assignments are consistent with the
dataset from the domain of high-voltage switchgear con- rules in π
. As already mentioned, in many configuration
figuration. The paper is concluded with an overview of scenarios users are not able or do not want to specify
future research issues (Section 5). values for all the defined variables in π but are interested
in recommendations that help to more easily complete a
Table 1
A simple example of a collection of already completed configuration sessions (one hot encoding). The abbreviation pdc denotes
a park distance control feature. Furthermore, fuel denotes the fuel consumption in gallons/100 miles. Finally, session current is
an ongoing session where variable settings for {π πππππ, 4-π€βπππ, πππππ} should be recommended.
attribute type pdc fuel skibag 4-wheel color
session city limo combi xdrive yes no 1.7 2.6 4.2 yes no yes no red blue
1 1 0 0 0 1 0 1 0 0 0 1 0 1 1 0
2 0 0 0 1 1 0 0 0 1 0 1 1 0 0 1
3 0 1 0 0 1 0 0 1 0 0 1 0 1 0 1
current 0 0 1 0 0 1 0 1 0 ? ? ? ? ? ?
configuration session [13]. We now introduce a definition 1β3 have already been completed. The current session
of a recommendation in the context of a configuration is ongoing and we are interested in a recommendation
task (see Definition 3). for the variables skibag, 4-wheel, and color. For the pur-
Definition 3. Given the definition of a configuration poses of this example and also for discussing the neural
task (π , π·, π
, π
πΈπ), a corresponding recommendation network based recommendation approach, we apply a
π
πΈπΆ = {π£π½ = π£πππ½ , .., π£πΏ = π£πππΏ } is a set of variable value one hot encoding of the configuration variables, for exam-
assignments of π£π β π. A recommendation π
πΈπΆ is consis- ple, in session 1, the configured car is of type city. In the
tent if π
πΈπΆ βͺ π
πΈπ βͺ π
is consistent, i.e., a solution can be scenario shown in Table 1, a simple case-based reason-
found. ing recommender would search for one or more nearest
neighbors (NN) and recommend the variable settings that
Example 3. π
πΈπΆ = {π πππππ = ππ, 4-π€βπππ = ππ, πππππ =
were choosen most often by the nearest neighbors. In
πππ}
our example, the nearest neighbor (session) of the current
Following the approach of case-based reasoning [16], session is session 3 (in terms of the number of equivalent
it can be the case that recommended variable value as- variable values). If we assume |NN|=1, we would recom-
signments are inconsistent with the already defined user mend π
πΈπΆ = {π πππππ = ππ, 4-π€βπππ = ππ, πππππ = πππ’π} to
requirements and the rules (constraints) defined in the the user in the current session (if we intend to predict all
knowledge base. This is the case if recommendations unspecified variable values at the same time).
are determined from already completed configuration Importantly, since the current user is interested in a
sessions without taking into account configuration con- car of type combi which requires the inclusion of a skibag
straints (rules in π
). In the following, we provide a simple (see Example 1), such a recommendation (π
πΈπΆ) induces
example of a case-based reasoning approach and then an inconsistency between the user requirements and the
focus on how to take into account configuration rules configuration knowledge base (the set of rules π
). A
in terms of a semantic regularization when optimizing a traditional approach to deal with such a situation is to
neural network responsible for recommending variable test the next recommendation for consistency and do this
settings. until a consistent recommendation could be found [13].
Our approach (that will be introduced in the following)
to deal with such a situation is to introduce a semantic
3. Recommending Configurable regularization into the neural network learning phase
Items which helps to avoid inconsistent recommendations as
far as possible.
As already sketched in the previous section, recommenda-
tions in the context of configuration scenarios are repre-
Neural Network based Recommendation Our ba-
sented by a set of attribute assignments, i.e., a recommen-
sic approach to recommend variable values in the context
dation could include a single attribute setting but also
of rule-based configuration is based on the feed-forward
numerous settings recommended at the same time. In this
neural network structure depicted in Figure 1. In such
section, we discuss different approaches to recommend
networks, the input layer consists of possible values (rep-
variable value settings in the context of knowledge-based
resented in terms of a one hot encoding) that have already
configuration scenarios.
been specified by a user. For example, the variable values
(preferences) that have already been specified by the user
Case-based Recommendation Table 1 represents a in session ππ’πππππ‘ are {π‘π¦ππ = πππππ, πππ = ππ, π π’ππ = 2.6}.
simple example of a set of already completed configu- Networks as those depicted in Figure 1 can be trained
rations that can be used as a basis for determining rec- in a domain-dependent fashion on the basis of a dataset
ommendations. In this example, configuration sessions
type = city π πππππ = π¦ππ (0.3)
π‘π¦ππ = ππππ skibag = no(0.7)
... ...
fuel = 1.7 color = red(0.8)
π π’ππ = 2.6 πππππ = πππ’π(0.2)
Figure 1: A simple neural network architecture with an input layer representing specified (e.g., type and fuel) and un-specified
(e.g., skibag andcolor) variables, one hidden layer, and an output layer that helps to estimate variable values of relevance.
comprised of already completed configuration sessions Constraint-Aware Recommendation For reasons
(see Sessions 1β3 in Table 1). Furthermore, the hidden of potentially inconsistent recommendations, we have
layer is used for learning dependencies between input introduced an enhanced neural network learning phase
values selected by the user and corresponding variable including a semantic regularization where inconsistent
values of potential relevance for the user. The number of recommendations are taken into account as regulariza-
nodes in the hidden layer is regarded as hyper-parameter tion term. In other words, although parts of the domain-
to be optimized in an item domain dependent fashion (in specific rules/constraints can be learned from the under-
[17] an equal amount of neurons in the input layer and lying training dataset, it can be the case that some or
the hidden layer showed the best performance). Finally, even many constraints are neglected and the resulting
the output layer supports a multi-branch approach (one variable value recommendations induce an inconsistency.
branch per variable) where each branch π is splitted into We denote this approach as constraint-aware neural net-
ππ output nodes representing the different domain val- works which are extremely relevant in recommendation
ues of variable π£π . In contrast to the input and hidden scenarios where domain-specific constraints/rules have
layer which use a ReLU activation function, classification to be taken into account by the recommender. To re-
is implemented using softmax. The choice of the train- duce the probability of inconsistent variable value rec-
ing hyper-parameters has been made based on several ommendations, knowledge base rules/constraints are
test executions. Optimizer βAdamβ [18] has shown the taken into account in the learning process. This goal
best performance compared to other gradient decent op- is achieved by integrating the results of a consistency
timizers like βADAGRADβ, βRMSPropβ and βSGDβ [19]. check of the proposed recommendation π
πΈπΆ (more pre-
βAdamβ uses adaptive estimation of first order and sec- cisely, π
πΈπ βͺ π
βͺ π
πΈπΆ) into a corresponding loss function
ond order moments, which slows down the adjustment as shown in Formula 1.
of neuron weights the more steps have been done. The
πΏ(π) β πΏπ‘ππππ (π) + Ξ©(π) + π Γ Ξ (π) (1)
selected parameters for the βAdamβ optimizer were an
initial learning rate of 0.001, π½1 = 0.9 and π½2 = 0.999. In this context, πΏ(π) denotes a loss function on the vec-
Please note that this network architecture assumes cate- tor π of weights in the neural network, πΏπ‘ππππ (π) denotes
gorical variables (e.g., similar to our Example 1) β other the prediction loss, Ξ©(π) represents a corresponding πΏ2
variable types require preprocessing such as binning or regularization term, π represents a hyper-parameter that
alternative architectures. Our neural network derived controls the impact of an inconsistency on the overall
from the knowledge base introduced in Section 2 consists loss, and Ξ (π) indicates whether the recommendation re-
of 6 output nodes and also 9 input nodes (assuming the sulting from π is consistent (0 is returned) or inconsistent
example from above). (1 is returned). As Ξ (π) is a discrete non-differentiable
In the basic version of our approach, neural networks function, the optimization of our loss function has to
are trained on the basis of training dataset (see, e.g., ses- resort to approximation of gradients via computation of
sions (1β3 in Table 1).The usage scenario of this basic finite differences.
neural network approach is the following: if a user inter-
acts with a configurator and has already specified a set of User Interaction and Knowledge Representation
initial requirements (π
πΈπ), the neural network can be ex- Our approach to neural network based variable value rec-
ploited for the recommendation of variable values. Since ommendation for knowledge-based configuration helps
the basic version of the neural network can only learn to reduce the probability of inconsistency-inducing rec-
constraints/rules from the available set of completed con- ommendations (see Section 4) and thus also can help to
figurations, it can be the case that predictions induce an make configuration processes less time-consuming for
inconsistency with the underlying rule set. users. The proposed recommendation approach is flexi-
ble in the sense that recommendations for single-variable
assignments as well as combined variable assignment
recommendations can be supported. In our working ex- veloped a case-based reasoning approach (see Section 3)
ample, we did not take into account settings, where a that recommends variable value settings on the basis of
configuration task is organized in phases where in each the preferences of the π nearest neighbors (in the given
phase a specific subcomponent of the product is config- setting, π = 1 achieved the highest prediction quality).
ured (e.g., software configuration as part of the configu- Furthermore, the two versions of the neural network
ration of a whole computer). On the user interface level, based approach have been implemented on the basis of
recommendations are mostly related to variables within the Keras API [21]. 2 The learning of the neural network
a specific phase. However, recommendations can also model is based on 32 iterations during the learning phase
be determined on the basis of existing variable settings of the model where 80% of the data is used for training
from different phases. purposes and 20% for testing. The first version of the
neural network model has been trained without taking
Recommendation Consistency The achievable de- into account the rules in the configuration knowledge
gree of recommendation consistency also depends on base whereas the learning of the model for the second
the used knowledge representation. In the case of a rule- version is based on the loss function included in Formula
based knowledge representation [6], it is not always fea- 1. For validation of the models they have been applied
sible to correctly predict if it is possible to complete a separately to 20 configurations which where not part of
partial configuration, i.e., given a (consistent) set of cus- the training or testing data.
tomer requirements, is it possible to find assignments
for the remaining variables in such a way that a consis- Prediction Quality Our first goal was to analyze the
tent and complete configuration can be achieved. If a prediction quality of the three variable value recommen-
more compact representation of all satisfiable variable dation approaches discussed in this paper: (1) case-based
assignments is available [20], our approach can be ap- recommendation, (2) neural network based recommenda-
plied to recommend the most relevant option among the tion, and (3) neural network based recommendation with
consistent ones. In a similar fashion, constraint-based semantic regularization. To measure the prediction qual-
approaches [5] can be applied to infer remaining vari- ity, precision has been chosen as the key performance
able assignments that are still consistent with π
πΈπ and indicator. The precision of the recommendation of a
π
. In the following, we present the results of an empir- configuration π΄π
ππ has been measured in terms of the
ical analysis of our constraint-aware recommendation share of predictions part of the configuration accepted by
approach using a real-world dataset from the domain of the user π΄ in relation to the total number of predictions
high-voltage switchgear configuration. contained in π΄π
ππ , see equation 2. In the context of our
evaluation, we were specifically interested in the predic-
tive performance depending on the number of already
4. Evaluation known attribute values. The prediction task was specified
in such a way that given a chosen set of input attributes
The configurator application for the high-voltage
(the known settings representing π
πΈπ), the task was to
switchgear domain has been developed with the goal to
predict all other missing attributes to complete the con-
reduce engineering effort during the offering stage of
figuration. Since our configurator is organized in 5 con-
these highly complex systems. The underlying dataset
figuration phases, the phase number of a to be predicted
includes π = 720 complete configurations developed by
variable had to be equal to the number of the current
skilled sales employees from Siemens Energy AG. Each
configuration phase and the phase number of known
entry of the dataset consists of π = 60 attribute settings
variables (π
πΈπ) had to be lower or equal to the phase
(assignments), i.e., each configuration is described by 60
number of the to be predicted variable. Consequently, the
variables (representing features and subcomponents). In
missing attributes were iterative predicted by selecting in
this context, 10 out of the 60 variables have been defined
each iteration the values for those attributes part of the
as basic switchgear features which are assumed to be
configuration phase with the lowest phase number. In
selected by the user before a recommendation can be
the following iteration the previously predicted variables
triggered (e.g., basic switchgear category to be installed).
have been utilized as known variables for predicting the
The focus of recommendation are the remaining 50 more
variables of the next configuration phase. This has been
specific features with a sometimes lower degree of un-
repeated till the configuration was complete.
derstandability where it is often an issue for users to find
good or even optimal settings (e.g., AC supply voltage).
The dataset is composed of consistent configurations that |π΄ β© π΄|
ππππ πππ’πππ‘πππ πππππππππππ‘πππ ππππππ πππ = π
ππ
have been built on the basis of a rule-based configuration |π΄π
ππ |
system.1 As a baseline in our evaluation, we have de- (2)
1 2
www.camos.de. https://github.com/MaUt89/ConLearn
100
avg. precision without SR
Figure 2 provides an overview of the outcome of our 95 avg. precision with SR
evaluation by averaging the precision achieved for each 90
avg. precision of CBR
of the 20 validation configurations applied to the models. 85
precision (%)
80
The results clearly indicate the potential of prediction
75
quality improvements that can be achieved by the in- 70
clusion of semantic regularization concepts. Whereas, 65
(1) case-based recommendation achieves only a preci- 60
sion of 60.5% when starting with ten initially specified 55
variable values, the neural network based recommenda- 0 10 20 30 40 50 60
number of already known variable values
tion approaches predict the variable values with 74% (2)
and 76.33% (3) precision. Noticeable is that, the neural Figure 2: Precision of high voltage switchgear related predic-
network based recommendation with semantic regular- tions (SR = semantic regularization, CBR = case-based reason-
ization (3) outperforms the approach without semantic ing).
regularization (2) in every validation scenario. Finally, avg. consistency without SR
with 50 out of 60 variable values given as an input for 100 avg. consistency with SR
avg. consistency of CBR
the configuration both neural network based approaches 99
reach a precision of 100%. 98
consistency (%)
97
96
Consistency We were also interested in the degree of 95
consistency of the determined recommendations π
πΈπΆ, 94
i.e., consistent(π
πΈπΆ βͺ π
πΈπ βͺ π
). The consistency of the 93
recommendation of a configuration π΄π
ππ has been mea- 0 10 20 30 40 50 60
sured in terms of the share of knowledge base consistent number of already known variable values
predictions π΄β part of the recommendation in relation
Figure 3: Consistency of high voltage switchgear related
to the total number of predictions contained in π΄π
ππ , see
predictions (SR = semantic regularization, CBR = case-based
equation 3. As can be seen in Figure 3, the semantic reg-
reasoning).
ularization helps to decrease the inconsistency degree of
recommendations (compared to the CBR and the basic
neural network based approach). Starting with a consis- rators and compared it with cased-based and basic neural
tency of 95.27% (ten variables values already known) the network based recommendation. To improve the predic-
neural network based approach with semantic regulariza- tion quality and consistency of recommendations, we
tion (3) reaches already with 30 initially known variable have introduced a semantic regularization approach that
values a consistency of 100%. Both other approaches (1) helps to further increase the consistency degree of rec-
ommendations especially in the context of rule-based
and (2) achieve poorer results with ten initially given vari-
able values 93.59% (1) and 94.88% (2) and reach a consis- configuration scenarios. The presented approach has
been integrated into a industrial rule-based configuration
tency of 100% not until 40 variables are initially specified.
All in all, the higher consistency of approach (3) includ-environment that focuses on the configuration of high-
ing the semantic regularization has been expected since voltage switchgears. The presented approach can in-
this approach is penalizing non-consistent predictions crease both, prediction quality and consistency of the de-
during the learning phase of the model. Nevertheless, termined recommendations. Furthermore, the approach
the impact could have been higher and the consistency is generalizable to other types of configuration knowl-
especially with a low number of initially known variable edge representations such as constraint satisfaction prob-
values is improvable. To achieve this an optimization lems (CSPs).
of the hyper-parameter π is desirable and part of future Future work will focus on the integration of the de-
work. veloped concepts into model-based configuration knowl-
edge representations such as CSPs [5]. Furthermore, we
β will extend the scope of considered machine learning ap-
|π΄ β© π΄π
ππ |
ππππ πππ’πππ‘πππ πππππππππππ‘πππ ππππ ππ π‘ππππ¦ = proaches a.o. with an integration of matrix factorization
|π΄π
ππ | based variable value prediction. The dataset size used for
(3)
the evaluation presented in this paper can be considered
as a limitation of this work, in particular w.r.t. neural
5. Conclusions and Future Work network models. A major focus of future work will be
the evaluation of our approach with larger industrial con-
We have introduced an approach to the integration of figuration datasets. The neural network based prediction
recommendation features into knowledge-based configu- models will also be evaluated for their applicability in
the context of diagnosis scenarios, i.e., scenarios where tion using the minimax decision criterion, Artificial
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