Applying Heuristic and Machine Learning Strategies to Product
Resolution
Oliver Strauß
1 a
, Ahmad Almheidat
2
and Holger Kett
1 b
1
Fraunhofer-Institute for Industrial Engineering IAO, Nobelstraße 12, 73760 Stuttgart, Germany
2
Institute of Human Factors and Technology Management, University of Stuttgart,
Nobelstraße 12, 73760 Stuttgart, Germany
Keywords:
Product Resolution, Duplicate Detection, Machine Learning, Classification Web and Social Media Analytics.
Abstract:
In order to analyze product data obtained from different web shops a process is needed to determine which
product descriptions refer to the same product (product resolution). Based on string similarity metrics and
existing product resolution approaches a new approach is presented with the following components: a) extrac-
tion of information from the unstructured product title extracted from the e-shops, b) inclusion of additional
information in the matching process, c) a method to compute a product similarity metric from the available
data, d) optimization and adaption of model parameters to the characteristics of the underlying data via a ge-
netic algorithm and e) a framework to automatically evaluate the matching method on the basis of realistic test
data. The approach achieved a precision of 0.946 and a recall of 0.673.
1 INTRODUCTION
Product resolution (also known as product matching,
or de-duplication) is the task of disambiguating dif-
ferent appearances of a product in various diverse
sources like e-shops. The use case that motivated this
work is the need to analyze product reviews obtained
from different e-shops via web-scraping. This is only
possible, if the same product can be identified in the
different sources in order to associate the collected re-
views with it. Product resolution is also relevant for
other e-Commerce applications like online search en-
gines, product data aggregation for web mining appli-
cations, and product tracking across different e-shops.
Relevant data for product resolution is not always
present as structured data on e-shop pages that typi-
cally contain a descriptive title, free text descriptions
and tables. Extracting such structured data (e.g. prod-
uct name, brand, model id, product number, units,
etc.) and normalizing the extracted values can sig-
nificantly improve matching precision. This paper fo-
cuses on product resolution based on the product title
and product attributes previously extracted from ta-
bles and descriptions.
In this paper we describe three heuristic methods
a
https://orcid.org/0000-0003-1421-2744
b
https://orcid.org/0000-0002-2361-9733
to extract structured information from unstructured
product titles like MIELE Trockner TMG 840 WP,
A+++, 8 kg: brand names (MIELE, Section 3.1.1),
unitized values (8 kg, Section 3.1.2) and potential
product ids (TMG 840 WP, Section 3.1.3). Further-
more we investigate a number of pre-filters based on
this and other available information in order to reduce
the number of needed comparison of product pairs
in the matching process (Section 3.2.2 and 5.1.1).
The actual classification is performed using two ap-
proaches that employ supervised learning: a heuristic
method based on a combination of string similarity
metrics with adjustable thresholds and weights which
are subsequently adapted to the characteristics of the
test data (Section 3.2.3) and a Random Forest classi-
fier that uses feature vectors constructed from combi-
nations of string similarities on different parts of the
product description. A Java-based framework to per-
form the matching experiments has been developed
and is described in Section 4.3. Results of various ex-
periments on two real world data sets are presented in
Section 5 followed by a short discussion and conclu-
sions in Sections 6 and 7.
The contribution of this paper is not an entirely
new method for product resolution but a new combi-
nation of exiting approaches and aims to help inform
other experiments on the effects and performance of
the prefilters and classification methods used.
242
Strauß, O., Almheidat, A. and Kett, H.
Applying Heuristic and Machine Learning Strategies to Product Resolution.
DOI: 10.5220/0008069402420249
In Proceedings of the 15th International Conference on Web Information Systems and Technologies (WEBIST 2019), pages 242-249
ISBN: 978-989-758-386-5
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

2 RELATED WORK
The problem to identify descriptions of the same
product in different web shops (product resolution)
is a special case of the well researched problem of
identifying duplicates in a set of records, see e.g. (El-
magarmid et al., 2007) for a summary. Most of these
methods use text similarity measures to compute doc-
ument similarities by measuring the degree of simi-
larity between words, sentences, paragraphs or even
whole documents. Since in the first phase of the
project mainly test data were available, which con-
tained only the product title, methods were first ana-
lyzed, which try to determine product matches exclu-
sively on the basis of this single piece of information.
(Vandic et al., 2012) use regular expressions to ex-
tract so-called ”model words” from the product title
that consist of both numeric and non-numeric charac-
ters and are potential candidates for product numbers.
The similarity is then calculated as a weighted aver-
age of the cosine similarity of the title and the average
Levenshtein similarity of the model words involved.
(van Bezu et al., 2015) extends this approach to in-
clude product attributes in the matching. Another ap-
proach to obtaining structured information from un-
structured product titles using regular expressions is
provided by (Horch et al., 2015). With the help of
a unit ontology, unit-related variables in the product
title are identified, extracted and made available for
product comparison.
Approaches to enrich the information contained in
product titles by web searches and subsequent addi-
tion of characteristics and features contained in the
search results are presented by (Gopalakrishnan et al.,
2012) and (Londhe et al., 2014). (Londhe et al., 2014)
use a graph based Community Detection algorithm
for matching, while (Gopalakrishnan et al., 2012) cal-
culate importance scores for title tokens from web
searches and use the Cosine similarity of token pairs
weighted by their importance.
Other more recent approaches use machine learn-
ing and neutral networks to approach product match-
ing. (Ristoski et al., 2016) use a Conditional random
Field model (CRF) in combination with text embed-
dings to extract product features from the product ti-
tle and description and compare the approach with a
dictionary based approach and a random forest classi-
fier (Breiman, 2001). (Ristoski and Mika, 2016) also
used named entity recognition to features as extract
key-value pairs for subsequent matching.
(Shah et al., 2018) use a classification based
approach using shallow neural network based on
fastText and a similarity based approach based an
Siamese networks to approach the product resolution
problem.
Microdata embedded in shop web sites is used by
(Petrovski et al., 2014) to match products using a bag-
of-words approach. (Petrovski et al., ) extends this
work with a genetic algorithm for learning regular ex-
pressions for feature extraction.
3 APPROACH
3.1 Pre-processing
The purpose of pre-processing is to prepare the data
in a way that makes the matching process as effi-
cient and precise as possible. In order to achieve this
goal pre-processing tries identify and extract semanti-
cally meaningful information from the input data. In
this paper we employ brand extraction (sec. 3.1.1),
unit extraction (sec. 3.1.2) and model word extrac-
tion (sec. 3.1.3) to this end. These pre-processing
steps extract data and remove it from the title. There-
fore the sequence of pre-processing operations mat-
ters. To achieve the best results, the operations brand
extraction and unit extraction should to be performed
first, since model word extraction partly matches the
same tokens but provides a weaker semantic context.
Therefore the pre-processing workflow applied to the
raw data looks as follows:
1. Trim leading an trainling whitespace
2. Perform brand extraction
3. Perform unit extraction
4. Perform model word extraction
5. Convert strings to lowercase
3.1.1 Brand Extraction
Brands are extracted from product titles using a
heuristic method based on the assumption that the
brand will be the first token in almost every title. The
method works as follows:
1. Extract the first token from every product title.
2. Normalize the tokens.
3. Build a histogram of the tokens.
4. A token t
i
is treated as brand, when its relative fre-
quency f
rel
(t
i
) = N
i
/N is above a specified thresh-
old f
min
value where N
i
is the number of oc-
curences of t
i
and N is the total number of tokens.
Experiments on the given test data yield good re-
sults for f
min
≈ 0.004. This value was determined by
manual inspection of the resulting brand list and sub-
sequent lowering of the threshold, until the first false
Applying Heuristic and Machine Learning Strategies to Product Resolution
243

positives were encountered. The concrete value de-
pends on the data used and has to be determined for
every data set. The described approach works best
with bigger data sets, since for small sets deviations
have much more influence on the result and lead to a
higher number of false positives.
Once a list of brands has been identified, a second
pass through all product titles is performed to extract
and/or remove brands from the title (even if they are
not the first token in the title).
3.1.2 Unit Extraction and Normalization
The purpose of unit extraction is to assign semantic
meaning to tokens in a product description that de-
scribe units of measurement as well a the measured
quantity. Simple examples are 8 kg or 1500 U/min.
In the following we call a quantity with an associ-
ated unit an amount. To be able to compare amounts
with different unit scales (e.g. mm and m) within one
unit category (e.g. weight or frequency), the amounts
have to be converted to a the common base unit in
this unit category. If this is the case, amounts can be
compared by just looking at the quantities. Each unit
category has a base unit that can be chosen accord-
ing to the commonality of the different scales in the
problem domain.
The logic that determines unit amounts is able to
handle the following cases:
• The amount is given as a simple number (e.g. 10
kg or 2.50 m)
• The amount is given as a fraction of two integer
numbers (e.g. 3/8 in or 50/60 Hz). Two cases
are distinguished: a) If the numbers do not have
a common divisor the amount is treated as a frac-
tion and is converted to a floating point number
(e.g. 3/8 ⇒ 0.375). b) If the numbers share a
common divisor the amount is treated as a range
(e.g. 50/60 ⇒ [50..60]).
• The amount is given as a range of two integer
numbers (e.g. 50-60 Hz): The amount is treated
as a range [50..60] in this case.
In addition to the units that are quantified by a
number, energy efficiency labels of the form A+++ are
supported as a special case.
3.1.3 Model Word Extraction
Model words are tokens of a product title that contain
both numeric and alphabetic/punctuation characters
(Vandic et al., 2012; de Bakker et al., 2013). These
tokens often represent product numbers of productIDs
and are therefore relevant to determine if two product
titles are matching. We slightly modified the above
mentioned approach in that we also check for adja-
cent tokens that are either model words or tokens that
contain either only upper case letters or only numeric
characters. Such adjacent tokens are subsequently
merged to one model word. Model words are ex-
tracted using a regular expressions and post processed
to merge adjacent model words into one word. As an
example the tokens MIELE, TMG, 840 and WP would
have been extracted from the title MIELE Trockner
TMG 840 WP, A+++, 8 kg. Since the last three ex-
tracted tokens are adjacent, the resulting model words
are MIELE and TMG840WP.
3.2 Matching Strategies
3.2.1 General Approach
To identify matching products in a set of N product
descriptions the following strategy is applied:
1. Formation of all possible pairings of product de-
scriptions. This results in N
2
product pairs. Under
the assumption, that the comparison function is
symmetric (i.e. that comparison of product A with
product B yields the same result as comparing B
to A) reduces the number of pairs to N(N − 1)/2.
2. For every pair of product descriptions the binary
decision has to be made whether the two descrip-
tions describe the same product or not. This deci-
sion can be made in several ways:
a. By means of hard criteria, e.g. ”Products are
the same when their titles exactly match ” (see
Section 3.2.2).
b. By means of similarity functions that are
mostly based on string similarity metrics (e.g.,
Levenshtein, Cosine, or Jaccard, see (Elma-
garmid et al., 2007)) that are combined in dif-
ferent ways and are subsequently applied on
different parts of the product description (see
Section 3.2.3). A similarity function returns
a value between 0 (different) and 1 (equal) to
which a threshold is subsequently applied. If
the similarity is above the threshold, products
are classified as equal, otherwise as different.
c. Using a learnt classifier (see Section 3.2.4).
3. Based on the classification result a clustering
strategy can optionally be applied to derive groups
of similar products from the set of pair compar-
isons.
3.2.2 Pre-filtering
Since product resolution is based on the pairwise
comparison of products, it has a complexity of O(N
2
)
WEBIST 2019 - 15th International Conference on Web Information Systems and Technologies
244

in number N of the products to be compared. To ac-
celerate the comparison and to allow comparisons of
larger product quantities filters can be applied to re-
duce the number of needed pair comparisons.
In principle, filters can be used in two ways: a) as
rejecting filters RF whose goal to reduce the number
of pair comparisons and b) as accepting filters AF that
can already be considered as part of the actual match-
ing process. They should use clear computationally
inexpensive decision criteria.
Performance measures for rejecting pre-filters are
pair completeness PC, reduction rate RR and the their
harmonic mean F
Filter
whereas accepting filter perfor-
mance can be measured with the usual measures pre-
cision P, recall R and their harmonic mean F
1
(see
Section 4.2). Pre-filters are a special case of blocking
strategies (see e.g. (K
¨
opcke, 2014)).
3.2.3 Rule-based Approach
The pursued rule-based approach is based on the
Title-Model-Word method described in (de Bakker
et al., 2013) ans (Vandic et al., 2012). This approach
combines multiple simple string similarity functions
to more complex functions. In the following, T
1
and
T
2
are the two product titles, and tok(T
1
) and tok(T
2
)
are the sets of tokens that each respective title is com-
posed of. The approach is comprised of the following
steps:
1. Comparison of brands and rejection of pairs
(T
1
,T
2
) with different brands.
2. Comparison of all product titles using a simple co-
sine similarity metric tCosSim(T
1
,T
2
) and accept
a product pair if the similarity is above a threshold
α.
3. Pairwise comparison of the model words (i.e. the
product number candidates) of the two products.
Comparison is performed by splitting each model
word into a letter part (containing all letters in the
model word) and a number part (containing all
numbers). Only model words with matching letter
and number parts are considered as match.
4. Breaking down the product title into individual to-
kens tok(T
1/2
) and calculating the mean Leven-
shtein similarity of all combinations of token pairs
avgLevSim(tok(T
1
),tok(T
2
)), where longer words
receive a higher weight. The resulting title sim-
ilarity tSim is the weighted sum of tCosSim and
avgLevSim with the adjustable weight β.
tSim(T
1
,T
2
,α) = β tCosSim(T
1
,T
2
)
+(1 − β) avgLevSim(tok(T
1
),tok(T
2
))
(1)
5. Calculation of the mean Levenshtein similar-
ity avgLevSim(MW
T 1
,MW
T 2
) of all model words
MW
T 1
und MW
T 2
. The resulting final similarity
tSim is the weighted sum of avgLevSim and tSim
with the adjustable weight β.
sim(T
1
,T
2
,δ) = δ avgLevSim(MW
T 1
,MW
T 2
)
+(1 − δ) tSim(T
1
,T
2
)
(2)
This approach only considers information that is
available in the product title and can therefore be ap-
plied to both the basic and extended datasets. In ad-
dition, steps 1. to 3. do not depend directly on the
language of the product descriptions. This generality
of the approach comes with a lower performance in
precision and recall.
3.2.4 Machine-learning Approach using a
Random Forest Classifier
In the learning phase a Random Forest classifier
(Breiman, 2001) creates a predetermined number
of decision trees, which evaluate randomly selected
components of a feature vector. In the classifica-
tion phase, the feature vectors of the product pairs to
match are classified by each of the generated decision
trees. The classification result is the majority vote of
all individual trees results.
The creation of meaningful feature vectors is es-
sential for good classificaton results. A feature vec-
tor describes the properties of a pair of product de-
scriptions. The vectors is composed of a number of
components which are represented as floating point
numbers. Each component encodes some information
about the product pair. Examples are:
• The similarity of the product title as measured by
some string similarity metric
• The similarity of the brand candidates
• The average similarity of all model word combi-
nations
• The maximum similarity of all model word com-
binations
• The degree of correspondence between unit and
their associated quantities
• The degree of agreement between the extracted
features of the two products
The Random Forest classifier is a supervised
learning method and, like the rule-based approach,
requires the presence of labels in the training data
that encode the information about the actual match-
ing products.
Applying Heuristic and Machine Learning Strategies to Product Resolution
245

4 EVALUATION
4.1 Test Data
The evaluation of our approach is based on two real
word data sets provided by an industrial partner:
• A basic data set that consists of 44,758 product
descriptions extracted from ten different web
shops. Covered languages are German, English,
French and Dutch and the data mainly consist
of the product titles (e.g. CANDY CH 647 B
Glaskeramik-Kochfeld (590 mm breit, 4
Kochfelder)). The products are from differ-
ent domains ranging from coffee machines to
washing machines.
• An extended data set that represents a subset of
the basic data set and consists of 3,629 product
descriptions from four German web shops. The
descriptions were enhanced with structured data
parsed from embedded micro formats and with
product properties in the form of key-value pairs
obtained by custom extractors (e.g. SKU, price,
currency, etc.).
All data was obtained by web scraping and was
subsequently labeled manually. Due to the data set’s
size the quality of labels could be fully checked. Sam-
ples have shown that the data set contains a limited
number of false labels but the error rate is compara-
tively low, especially in the extended data set.
4.2 Evaluation Metrics
Product resolution is a binary classification problem
with two possible outputs (match / no match). In
order to measure the quality of a product resolution
approaches, the standard binary classification metrics
precision P, recall R and the F
1
-measure can therefore
be used (see e.g. (Elmagarmid et al., 2007)).
For pre-filters used to narrow down the number
of computationally expensive comparisons, we fol-
low (K
¨
opcke, 2014) to use the pairs completeness
PC = M
f
/M with M
f
being the truly matching pairs
after filtering and M the total matching pairs. PC indi-
cates, how many true matches have been retained by
the filter.
The reduction ratio RR = 1 − c/pn indicates the
deduction of the search space. Here c is the number of
pairs after filtering and pn is the number of total pairs
that here is given by pn = N(N − 1)/2 with N being
the number of products to compare, since products are
only compared to products with higher id.
The corresponding F-measure F
Filter
= 2 · (PC ·
RR)/(PC +RR) is given as the harmonic mean of PC
und RR.
4.3 Evaluation Framework
To perform the matching exeriments a modular and
extensible framework for the execution and evalua-
tion of product resolution algorithms has been imple-
mented in Java 8 that automates the following steps of
the product resolution process:
1. Data Pre-processing as described in Section 3.1
2. Generation of product pairs for comparison: For
N product descriptions N(N − 1)/2 pairs are gen-
erated, since each pair is only considered once.
3. Outer Filter Cascade: A sequence of accepting
and rejecting filters is applied to the product pairs.
Pairs for which the filters can come to a decision
are added to the final result. Undecided pairs are
deferred to the next steps. The outer filter cas-
cade reduces the number of pairs that need to be
processed in the classification step. However the
filters remove information from the training data,
which could potentially decrease learning perfor-
mance.
4. Cross Validation: The classification performed in
the following steps 5.-7. is evaluated using an n-
fold cross validation strategy. The pairs are di-
vided into n random parts of about the same size.
Training and classification is now performed n
times, with one part being used as test data and all
other parts as training data. In this way all pairs
were used once for classification and (n−1) times
for training.
5. Training: In case of the rule-based approach
(see Section 3.2.3) the training step consists
of the optimization of the adjustable parame-
ters (threshold and weights) in the similarity
function. The framework supports optimization
by Mesh-based Optimization Genetic Algorithms
(Wilhelmst
¨
otter, 2018), Bound Optimization by
Quadratic Approximation (BOBYQA) (Powell,
2009) and Covariance Matrix Adaptation Evo-
lution Strategy (CMA-ES) (Auger and Hansen,
2005). In case of the machine learning approach
training is performed according to the Random
Forest algorithm (see Section 3.2.4).
6. Inner Filter Cascade: A sequence of accepting fil-
ters can be applied after rule-based or learnt clas-
sification if precise and efficient criteria to find
matching pairs are available.
7. Classification: In this step, the trained rule-based
or machine learning classifier is applied to the re-
maining product pairs and the final classification
decision is arrived at.
8. Summarizing and documentation of the results.
WEBIST 2019 - 15th International Conference on Web Information Systems and Technologies
246

5 RESULTS
5.1 Extended Data Set
The results in this section are based on the 3,629 prod-
uct description of the extended data set, for which
product features and structured data from embedded
micro formats were available.
5.1.1 Pre-filter Results for the Extended Data
Set
The following rejecting pre-filters (RF) have been
considered:
• RF
Brand
: Pairs with differing brand are rejected.
• RF
GT IN13
: Pairs with defined but different
GTIN13-value are rejected.
• RF
SKU
: Pairs with defined but different SKU-
value are rejected.
• RF
MW Pre f ix3
: Pairs that have no model words pair
with matching prefixes of length 3 are rejected.
• RF
FuzzyMW 5
: Pairs where all model word pairs
have a Levenshtein distance greater than 5 are re-
jected.
The performance of these rejecting pre-filters is
summarized in Table 1.
The following accepting pre-filters (AF) have
been considered:
• AF
GT IN13/SKU
: Pairs with defined and matching
GTIN13 or SKU values are considered a match.
• AF
MW 8
: Pairs with perfectly matching model
words of length > 8 are considered a match.
• AF
Title
: Pairs with perfectly matching normalized
titles are considered a match.
• Cascade of AF
GT IN13/SKU
, AF
MW 8
und AF
Title
.
The performance of these accepting pre-filters is
summarized in Table 2.
Accepting and rejecting filters can be combined.
The order in which the filters are applied is signif-
icant, since filters are processed in sequence where
each subsequent filter is applied only to the pairs that
have not been decided by the previous filters. Ta-
ble 3 shows the performance of a sequence of two
Table 1: Rejecting pre-filter performance.
Filter PC RR F
Filter
RF
Brand
98.61 93.39 95.93
RF
GT IN13
98.72 7.41 13.78
RF
SKU
83.87 33.51 47.89
RF
MW Pre f ix3
95.62 74.03 83.45
RF
FuzzyMW 5
75.53 97.71 85.20
accepting and one rejecting filters (AF
GT IN13/SKU
→
AF
MW 8
→ RF
Brand
). Only three product pairings were
wrongly marked as matching by the filters and nine
actual pairings were not found. The number of pair
comparisons left for classification is reduced from
6,147,612 to only 434,789.
Table 2: Performance of various accepting pre-filters. The
values refer to the decisions of the pre-filter and not to the
total result of the matching process hence FN and T N are
0.
Filter P T P FP FN T N
AF
GT IN13/SKU
100.00 359 0 0 0
AF
MW 8
99.32 436 3 0 0
AF
Title
95.28 101 5 0 0
AF
GT IN13/SKU
→ AF
MW 8
→
AF
Title
98.70 608 8 0 0
Table 3: Performence of the filter combination
AF
GT IN13/SKU
→ AF
MW 8
→ RF
Brand
.
Filter P R F
1
AF
GT IN13/SKU
→
AF
MW 8
→ RF
Brand
99.50 98.50 99.00
5.1.2 Classification Results for the Extended
Data Set
Four experiment based on the extended data set were
performed:
1. Exp
1
: The rule-based approach described in
Section 3.2.3 was combined with the filters
AF
GT IN13/SKU
→ AF
MW 8
→ RF
Brand
in the outer
filter cascade (see also Table 3).
2. Exp
2
: The Random Forest classifier was com-
bined with the filters AF
GT IN13/SKU
→ AF
MW 8
→
RF
Brand
in the outer filter cascade.
3. Exp
3
: Random Forest classifier was combined
with the filters RF
GT IN13/SKU
→ RF
GT IN13
→
RF
SKU
→ RF
Brand
→ AF
Title
in the outer filter cas-
cade.
4. Exp
2
: The Random Forest classifier was com-
bined with the filters AF
GT IN13/SKU
→ RF
Brand
→
AF
MW 8
the outer filter cascade.
The results are shown in Table 4. It can be
observed that the Random Forest method (Exp
2
)
achieves a significantly higher precision of P =
98.51% compared to the rule-based method (Exp
1
)
with P = 54.41%. However, the rule-based method
(Exp
1
) achieved a higher recall of R = 68.59% com-
pared to the R = 63.46% of the random forest clas-
sifier (Exp
2
). The selection of filters and their order
Applying Heuristic and Machine Learning Strategies to Product Resolution
247

have a significant impact on the overall performance.
An experiment with stronger rejecting filters (Exp
3
)
was able to increase the precision again to 99.7% at
the expense of the recall. In this run, only a single
product pair was incorrectly classified as matching.
By interchanging the last two filters of experiment
Exp
2
, experiment Exp
4
achieved the most balanced
result with the best value for the F
1
measure.
Table 4: Performance of the four experiments Exp
1−4
per-
formed on the extended data set.
Filter P R F
1
Exp
1
54.41 68.59 60.68
Exp
2
98.51 63.46 77.19
Exp
3
99.73 38.78 55.84
Exp
4
94.59 67.27 78.63
5.2 Basic Data Set
The results in this section are based on the 44,758
product descriptions of the basic data set that provide
only product titles for matching. To reduce the num-
ber of pair comparisons the product descriptions were
split based on the brand extracted from the title. This
resulted in 34 parts of different sizes, all of which,
however, remained manageable. The effects of this
split were a pair completion of PC = 99.26, a reduc-
tion ratio of RR = 93.37 and F
Filter
= 96.22. Only
0.74% of actual matches were lost, but the number
of comparisons was reduced from over one billion to
less than 66.5 million.
The experiment conducted used pre-filtering with
AF
Title
→ AF
MW 8
→ RF
MW Pre f ix3
→ RF
FuzzyMW 5
and
classification with the Random Forest classifier. It re-
sulted in a precision of P = 87.28, a recall of R =
61.89 and F
1
= 72.43.
Compared with the results of the extended data
set, which provides much more information about the
products that can be exploited, the result is clearly
worse. The additional information leads to a lower
number of false positives and thus increases the pre-
cision of the matching to P = 98.51% for the ex-
tended data. The recall is similar for both data sets
(R = 63.46% for the extended data set vs. R = 61.89%
for the basic data set).
6 DISCUSSION
The experiments have confirmed that additional struc-
tured data can improve the precision of the match-
ing. Comparison of the basic data set (only unstruc-
tured product title) and the extended data set (includ-
ing product properties and metadata) resulted in an in-
crease of precision from P = 87.3% to P = 98.5% and
recall from R = 61.9% to R = 63.5%. An even higher
precision is to be expected when the names of prod-
uct properties are not in the form of shop-dependent
strings but unified with shop-independent identifiers.
The Random Forest classifier achieved a higher pre-
cision of (P = 98.5%) compared to the rule-based
approach (P = 54.4%). The recall is slightly lower
though (R = 63.5% compared to R = 68.6%). The
best compromise was with a precision of P = 94.6%,
a recall of R = 67.3% and an F
1
-value of F
1
= 78.6%.
The experiments confirmed the big influence of
pre-processing (in particular the extraction of struc-
tured information from unstructured text) on the re-
sults of the matching process. Prefiltering can reduce
the number of comparison pairs significantly thus
making the compute-intensive matching of a large
number of products possible but there is a conflict of
objectives between the reduction rate and the num-
ber of actual matches lost in the filtering. The right
balance depends on the use case and the number of
products to compare. When applied to big data vol-
umes the presented methods reach their limit rela-
tively quickly (n ≈ 50,000) due to the O(n
2
) nature
of the matching problem. Prefiltering can only partly
solve this problem, since it does not change the nature
of the problem, but only reduces the n.
7 CONCLUSIONS AND FUTURE
WORK
With the developed method a fully automated product
comparison process can be implemented, provided
the expected errors are tolerable. If high precision
is required, the Random Forest-based method yields
very good results, as only less than two percent false
positives are to be expected. All tested approaches
show a recall of approx. 60 − 68%. If the use case
tolerates that not all matching products are found, the
approach is suited for automatic use.
The integration of our product comparison ap-
proaches into a semi-automated process requires the
presence of confidence information for each classi-
fication decision in order for the system to decide
which match decisions should be reviewed by a hu-
man expert. In the described evaluation framework
this is currently not directly possible. In the heuristic
approach confidence information can be derived from
the calculated similarities and for the Random For-
est method it can be obtained from a posteriori values
provided by the classifier for each classification deci-
sion.
Next steps in the context of the presented use
WEBIST 2019 - 15th International Conference on Web Information Systems and Technologies
248

case is the introduction of confidence values and the
comparison of the obtained result with similar ap-
proaches by performing the described experiments
and the datasets of the WDC Gold Standards for prod-
uct matching (Petrovski et al., 2017). Future work
will be the application of word embeddings (Penning-
ton et al., 2014) and character embeddings similar to
(Ristoski et al., 2016) the problem of product resolu-
tion and to combine these approaches with the prepro-
cessing and filtering methods described in this paper.
ACKNOWLEDGEMENTS
This work has been made possible by the Eurostars
project E!10138 ReProsis ”Big Data Product Anal-
ysis in Real Time – Product Management System
for International Markets”, sub-project ”Intelligent
Product Data Extraction and Product Resolution”
funded by the German Bundesministerium f
¨
ur Bil-
dung und Forschung (BMBF) under the grant number
01QE1632B.
REFERENCES
Auger, A. and Hansen, N. (2005). A Restart CMA Evo-
lution Strategy With Increasing Population Size. In
2005 IEEE Congress on Evolutionary Computation,
volume 2, pages 1769–1776, Edinburgh, Scotland,
UK. IEEE.
Breiman, L. (2001). Random Forests. Machine Learning,
45(1):5–32.
de Bakker, M., Frasincar, F., and Vandic, D. (2013). A
hybrid model words-driven approach for web product
duplicate detection. In Advanced Information Systems
Engineering, volume 7908 of Lecture Notes in Com-
puter Science, pages 149–161. Springer, Berlin, Hei-
delberg.
Elmagarmid, A. K., Ipeirotis, P. G., and Verykios, V. S.
(2007). Duplicate Record Detection: A Survey. IEEE
Transactions on Knowledge and Data Engineering,
19(1):1–16.
Gopalakrishnan, V., Iyengar, S. P., Madaan, A., Rastogi, R.,
and Sengamedu, S. (2012). Matching product titles
using web-based enrichment. In Proceedings of the
21st ACM International Conference on Information
and Knowledge Management, pages 605–614, Maui,
Hawaii, USA. ACM.
Horch, A., Kett, H., and Weisbecker, A. (2015). Extracting
Product Unit Attributes from Product Offers by Us-
ing an Ontology. In Proceedings of The Second Inter-
national Conference on Computer Science, Computer
Engineering, & Social Media, Lodz, Poland. IEEE.
K
¨
opcke, H. (2014). Object Matching on Real-World Prob-
lems. Dissertation, Universit
¨
at Leipzig, Leipzig.
Londhe, N., Gopalakrishnan, V., Zhang, A., Ngo, H. Q.,
and Srihari, R. (2014). Matching titles with cross ti-
tle web-search enrichment and community detection.
Proceedings of the VLDB Endowment, 7(12):1167–
1178.
Pennington, J., Socher, R., and Manning, C. (2014). Glove:
Global Vectors for Word Representation. In Proceed-
ings of the 2014 Conference on Empirical Methods
in Natural Language Processing, pages 1532–1543,
Doha, Qatar. Association for Computational Linguis-
tics.
Petrovski, P., Bryl, V., and Bizer, C. Learning Regular Ex-
pressions for the Extraction of Product Attributes from
E-commerce Microdata. page 10.
Petrovski, P., Bryl, V., and Bizer, C. (2014). Integrat-
ing product data from websites offering microdata
markup. In Proceedings of the 23rd International
Conference on World Wide Web - WWW ’14 Compan-
ion, pages 1299–1304, Seoul, Korea. ACM Press.
Petrovski, P., Primpeli, A., Meusel, R., and Bizer, C. (2017).
The WDC Gold Standards for Product Feature Extrac-
tion and Product Matching. In Bridge, D. and Stuck-
enschmidt, H., editors, E-Commerce and Web Tech-
nologies, volume 278, pages 73–86. Springer Interna-
tional Publishing, Cham.
Powell, M. J. D. (2009). The BOBYQA algorithm for
bound constrained optimization without derivatives.
Technical Report, University of Cambridge, Cam-
bridge, UK.
Ristoski, P. and Mika, P. (2016). Enriching Product Ads
with Metadata from HTML Annotations. In Pro-
ceedings of the 13th International Conference on The
Semantic Web. Latest Advances and New Domains
- Volume 9678, pages 151–167, Berlin, Heidelberg.
Springer-Verlag.
Ristoski, P., Petrovski, P., Mika, P., and Paulheim, H.
(2016). A machine learning approach for product
matching and categorization: Use case: Enriching
product ads with semantic structured data. Semantic
Web, 9(5):707–728.
Shah, K., Kopru, S., and Ruvini, J. D. (2018). Neural
Network based Extreme Classification and Similarity
Models for Product Matching. In Proceedings of the
2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Hu-
man Language Technologies, Volume 3 (Industry Pa-
pers), pages 8–15, New Orleans - Louisiana. Associa-
tion for Computational Linguistics.
van Bezu, R., Borst, S., Rijkse, R., Verhagen, J., Vandic, D.,
and Frasincar, F. (2015). Multi-component similarity
method for web product duplicate detection. In Pro-
ceedings of the 30th Annual ACM Symposium on Ap-
plied Computing, pages 761–768, Salamanca, Spain.
ACM Press.
Vandic, D., Van Dam, J.-W., and Frasincar, F. (2012).
Faceted product search powered by the Semantic Web.
Decision Support Systems, 53(3):425–437.
Wilhelmst
¨
otter, F. (2018). Jenetics - Library User’s Manual
4.3. Technical Report, Vienna, Austria.
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