The k Closest Resemblance Classifier for Amazon Products
Recommender System
Nabil Belacel
1,2 a
, Guangze Wei
2
and Yassine Bouslimani
2 b
1
Digital Technologies Research Centre, National Research Center, Ottawa, Ontario, Canada
2
Department of Electrical Engineering, Moncton University, New Brunswick, Canada
Keywords:
Machine Learning, Supervised Learning, Classifier, Information Retrieval, Content Based Filtering,
Recommender System.
Abstract:
This paper presents the application of classification method based on outranking approach to Content Based
Filtering (CBF) recommendation system. CBF intends to recommend items similar to those a given user would
have liked in the past by first extracting traditional content features such as keywords and then predicts user
preferences. Therefore content based filtering system recommends an item to a user based upon a description
of the item and a profile of the user’s interests. Typically, to represent user’s and items’ profiles the existing
CBF recommendation systems use the vector space model with basic term frequency and inverse document
frequency (tfidf ) weighting. The tfidf and cosine similarity techniques are able, in some cases, to obtain good
performances, however, they do not handle imprecision of features’ scores and they allow the compensation
between features which will lead to bad results. This paper introduces k Closest resemblance classifier for
CBF. The detailed models in this paper were tested and compared with the well-known tfidf based the k
Nearest Neighbor classifier using Amazon fine food and book reviews data-set. The preliminary results show
that our proposed model can substantially improve personalized recommendation of items described with short
text like products description and customers’ review.
1 INTRODUCTION
Personalized recommendation systems have been re-
garded as an important mechanism to overcome the
difficulty of information overload. They have become
an important research area for both academia and in-
dustry. Based on the historical records of users, the
recommendation system will initially recommend the
information to them for choosing according to their
preferences. In general, recommendation systems use
mainly three type of filtering techniques: the content
based filtering (CBF), the collaborative filtering (CF)
and hybrid filtering (the combination of CBF and CF)
(Adomavicius and Tuzhilin, 2005). The CBF tech-
nique recommends specific items that are similar to
those have been already positively rated in the past by
the active user. It uses only the content of items in
order to make a recommendation (Lops et al., 2011).
The CF system recommends items that are preferred
in the past by similar users to the active user. So, CF
a
https://orcid.org/0000-0003-1731-3225
b
https://orcid.org/0000-0003-2894-5113
techniques make the assumption that the active users
will be interested in items that users similar to them
have rated highly. The hybrid based filtering tech-
niques recommend items by combining CF and CBF
(Adomavicius and Tuzhilin, 2005). In this paper we
will focus on the CBF that compares the user‘s profile
to some reference characteristics to predict whether
the user would be interested in unseen items or not. It
uses textual analysis to generate the user’s and items’
profile. The CBF made recommendation based on
the user interests profile using features (keywords) ex-
tracted from the content of items previously rated by
that user (Lops et al., 2011). To recommend items to
user, the CBF follows three main functions: - updat-
ing the user profile; -2- filtering the available items
with user’s profiles; 3- recommending the items that
better fit the profile (Castro et al., 2014). The im-
proved method for CBF will be discussed and com-
pared with the traditional methods. The proposed ap-
proach is illustrated using the case study of a recom-
mender system for amazon products’ description and
customers’ review.
Belacel, N., Wei, G. and Bouslimani, Y.
The k Closest Resemblance Classifier for Amazon Products Recommender System.
DOI: 10.5220/0009155108730880
In Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) - Volume 2, pages 873-880
ISBN: 978-989-758-395-7; ISSN: 2184-433X
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
873
2 RELATED WORK
In CF methods, recommendation is based on other
users’ preferences with the assumption that similar
customers are more likely to have interest in same
products. Thus the similarity between different cus-
tomers is calculated based on their purchasing, view-
ing or any relevant usage history, but the indigenous
properties of each product are not considered. By
contrast, CBF use information about an item itself
to make suggestions, and most often, contents are in
plain text form. CF and CBF both have their pros
and cons. CBF have the ability to recommend new
items even if there is no rating provided by other
users. Or in the case where the user does not want
to share her/his information with other users for pri-
vacy and security reasons. The CBF technique has the
advantage in giving explanations on the recommen-
dation results. In meantime, it suffers from several
difficulties as presented in the paper (Adomavicius
and Tuzhilin, 2005). CBF techniques depend only
on items’ features. So, they require rich description
of items before recommendation can be made. Con-
sequently, the effectiveness of CBF depends on the
availability of descriptive data. In the case we do not
have enough user community with their rating history
and we need to use some attributes of products’ re-
views and description rather than using only the prod-
ucts’ ratings, we are facing CBF.
Customer reviews, opinions and shared experi-
ence are always important for marketing uses, and
sometimes take a vital position in enterprises’ deci-
sion making. A good understanding of customers can
help enterprises better grasp the market and generate
more profits while omitting customers’ useful feed-
back means missing out the opportunity. However,
despite the importance and value of such information,
there is lack of comprehensive mechanism that for-
malizes the opinions selection and retrieval process
and the utilization of retrieved opinions due to the dif-
ficulty of extracting information from text data (Aciar
et al., 2006; Li et al., 2017). In this work we are fo-
cusing only on the CBF system that is based on the
content of the items. To recommend items, CBF first
builds the user interest profile by using contents and
description of items previously rated by this user and
then it filters out the recommended items that would
fit the user preferences and interests.
The outline of CBF approach, as presented in
Fig.1, consists of three steps (Lops et al., 2011):
1. Content analyzer In this step the relevant informa-
tion is extracted from the text describing the items
(product description). The items’ documents are
presented by list of keywords or terms. To extract
Figure 1: A general framework of CBF.
the features from the text some pre-processing
steps are needed. First, We reduce the text to
lower case character and removing all types of
punctuation; secondly, tokens extraction or tok-
enization, where tokens are maximal sequence of
non-blank characters. Third, we consider word
stems as index terms (Tokens stemming) using
Porter stemmer (Porter, 1980); and then removing
the common English stop words (words that oc-
cur very often and are not relevant for discrimina-
tion). At the end a feature selection will be applied
to select from the original term set (a set contain-
ing all the terms from items’ documents); a subset
such that only the most representative terms are
used. The obtained subset will be used to generate
items’ profiles where each item will be presented
by a set of important terms.
2. User Profile From the items’ profiles generated in
the previous phase, the user profile is constructed
by taking into account the items that user liked or
disliked in the past. In this phase, many machine
learning algorithms can be used to learn the user
profile (Lops et al., 2011).
3. Filtering process
Then, the system searches for relevant items by
comparing the user profile with contents of the
query items. And then, the system recommends
items to the user that are the most similar to the
items of the class representing the liked items of
that user, the class "LIKE".
In the next section we will present a most used
CBF system that uses models from information re-
trieval(Belkin and Croft, 1992) known as Keyword
based Vector Space Model. We will use this approach
as baseline in the experiments in order to compare
with our proposed method.
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
874
The CBF using Vector Space Modeling (VSM)
follows the same process of text classification by cat-
egorizing documents into predefined thematic cate-
gories. This is often uses the supervised learning and
conducted in two main phases: the document index-
ing and classifier learning (Sebastiani, 2002): In doc-
ument indexing the numeric representation of docu-
ment is created by applying the two steps on docu-
ments: first a subset of terms from all terms occur-
ring in the whole collection is selected and then term
weighting is calculated by assigning a numeric value
to each term in order to build the profiles of docu-
ments based on its contribution to each document. In
the classifier learning a document classifier is devel-
oped by learning from the numeric representations of
the documents.
In information retrieval and VSM, term weight-
ing is formulated as term frequency.inverse docu-
ments frequency known as t f id f . The t f id f is one of
the most popular term-weighting techniques for CBF
(Lops et al., 2011). For instance, 83% of content-
based recommender systems in the domain of digital
libraries use t f id f (Beel et al., 2016). CBF using Vec-
tor Space Model (VSM) is often conducted in three
phases:
1. Feature extraction, each product is represented
by a subset of terms from all terms occurring in
the items collection
2. Term weighting, the items’ features are weighted
using the most common weighting method in the
VSM known as term frequency-inverse document
frequency t f id f method. The t f gives a local
view of term, expresses the assumption that mul-
tiple appearance of term in a document are no less
important than single appearance. The id f gives
a global view of terms across the entire collec-
tion assuming that rare terms are no less important
than frequent term. For more details on t f id f for
CBF the readers can refer to (Pazzani and Billsus,
2007; Lops et al., 2011). In t fid f the user pro-
file is represented by a vector of weights where
each component denotes the importance of term
to user.
3. k-nearest neighbor classifier and cosine simi-
larity measure, from the two above phases, the
user profile and the content of new items are rep-
resented as t f id f vectors of terms’ weight. The
CBF system calculates the similarity between the
documents previously seen and rated by the user
and the new document. Prediction of user’s inter-
est in particular document is obtained by cosine
similarity. As pointed out in the reference (Lops
et al., 2011) cosine similarity is most widely used
to determine the closeness between two docu-
ments.
The k-Nearest Neighbor (k-NN) is a classical
method for recommender systems (Lops et al., 2011).
k-NN is a basic machine learning algorithm used for
classification problems. It compares the new item
with all stored labeled items in training set using the
cosine similarity measure and determines the k near-
est neighbors. The class label of the testing item or
new item can be determined from the class labels of
the nearest neighbor in the training set. Each item
in the training set is presented by a weighted vector,
which each component j presents the t fid f of corre-
sponding term t
j
. For each item in the testing we cal-
culate the t f id f on the m terms selected from training
set. The training phase of the algorithm consists only
of storing the attribute vectors with their class label
in memory. k-NN algorithm compares the all stored
items to query item using a cosine similarity function
and determine the k nearest neighbors. A majority
voting rule is applied to assign a query item to a class
"LIKE" or "DISLIKE". The k-NN classifier is one of
the successful techniques for CBF.
Although k-NN classifier has been successfully
applied to some CBF applications, it suffers from
some limitations such as: it requires a high compu-
tation time because it needs to compute distance of
each query item to all training items (it does not have
a true training phase, all the training set is used); the
pre-processing, normalization or change of input do-
main is often required to bring all the input data to
the same absolute scale. The number k in the k-NN is
given a priory. So, if one changes the number k, the
assignment decision may be also changed. To address
these issues a k-CR was introduced. In the following
section, we describe our CBF based k-CR.
3 METHODOLOGY
Our methodology follows the same process of text
classification by categorizing documents into prede-
fined thematic categories. This is often uses the su-
pervised learning and conducted in two main phases:
the document indexing and classifier learning (Sebas-
tiani, 2002): In document indexing the numeric repre-
sentation of document is created by applying the two
steps on documents: first a subset of terms from all
terms occurring in the whole collection is selected and
then term weighting is calculated by assigning a nu-
meric value to each term in order to build the profiles
of documents based on its contribution to each docu-
ment. In the classifier learning a document classifier
is developed by learning from the numeric represen-
The k Closest Resemblance Classifier for Amazon Products Recommender System
875
tations of the documents. The outline of our proposed
CBF approach consists of the three steps as presented
in Fig.2):
3.1 Content Analyzer
A list of terms are extracted from the item’s docu-
ment. Each item is described by set of most impor-
tant terms. After the pre-processing was done, the
100 more frequent words are extracted to generate the
items’ profiles.
3.2 User Profile/ Learning a User Model
The contents of items are converted to structured data
by selecting the 100 attributes generated in content
analyzer component. From the structured histori-
cal data a classification learning is applied to build
user’s preference model. Therefore, the training data
is divided into two classes:- class "LIKE": the items
that user likes; class "DISLIKE": the items that user
doesn’t like. In this work, we have proposed new
CBF methodology based on the k-Closest Resem-
blance classifier.
k-Closest Resemblance classification method:
The k-Closest Resemblance (k-CR) is a prototype
based classification method. The k-CR method is
based on the scoring function to determine a sub-
set of prototypes representing the closest resemblance
with an item to be classified (Belacel, 1999; Belacel,
2004). It applies the majority-voting rule to assign an
item to a class. The scoring function is based on out-
ranking approach developed by Roy (Roy, 2013) and
following the same methodology of PROAFTN classi-
fier presented in (Belacel, 2000; Belacel, 2004; Bela-
cel and Cuperlovic-Culf, 2019). The k-CR procedure
proceeds in two phases:
1. Prototypes Learning: For each class C
h
(h = 1
presenting the class "LIKE" AND h = 2 present-
ing the class "DISLIKE", k-CR determines a set
of prototypes or items’ profiles. For class "LIKE"
the prototype is representing by the vector (b
h
),
h = 1 and for the class "DISLIKE" the prototype
is representing by the vector (b
h
), h = 2. The pro-
files are considered as good representative of their
class and are described by the score of the n fea-
tures. The profile of the recommended items rep-
resenting the class "LIKE" and not-recommended
items representing the class "DISLIKE". In the
learning phase we determine for each class a ref-
erence profile. The profile (b
1
) representing the
profile of items that the target user likes and (b
2
)
representing the profile of the items that the tar-
get user dislikes. To determine the profile of
each class, we use the training set that contains
set of items that the user already seen and rated.
More precisely for each profile and each feature
of each class, an interval is determined. To define
these intervals k-CR follows the same discretiza-
tion approach for learning PROAFTN classifier
described in (Belacel and Cuperlovic-Culf, 2019).
Once the prototypes of the classes are built, k-CR
will proceed to phase 2.
2. Prediction/Classification: To classify an unla-
beled sample, the k-CR determines the smallest
possible subset of prototypes which are closest to
an item to be classified. Based on this subset of
closest prototypes, the decision to classify or not
an item to a class is made by applying the same
majority voting rule used in k-nearest neighbor
classifier. To classify an item to LIKE or DIS-
LIKE class, k-CR applies the following steps:
1. Preference Relation between the Prototypes: k-
CR takes as input the partial distance and sim-
ilarities induced by the the set of features and
aggregates them into global preference relation
P
s
(b
h
, b
l
), b
h
and b
l
represent two prototypes of
classes C
l
and C
h
respectively. The preference re-
lation P
s
expresses the degree with which the re-
semblance between the item s and the prototype
b
h
is stronger than the resemblance between the
item s and the prototype b
l
.
2. Scoring Function: Based on the preference rela-
tions P
s
between the whole prototypes of classes,
k-CR selects the best prototypes in terms of their
distance or resemblance with the item to be classi-
fied. The scoring function is used to select a sub-
set of prototypes, eventually reduced to one pro-
totype, that are more closely to the item s. Differ-
ent scoring functions can be used, for more details
please see (Belacel, 1999; Belacel, 2004);
3. Assignment Decision/Class Prediction: Once a
subset of k closest resemblance prototypes pre-
senting the items’ profiles to the query item s is
determined, a majority voting rule is applied to as-
sign the item s to the closest class. More details on
k-CR are given in (Belacel, 1999; Belacel, 2004;
Belacel and Boulassel, 2004).
3.3 Recommendation
The k-CR procedure ranks the items in the class
"LIKE" from the best to the worst. Then, based on
the ranking, our CBF system selects the N-top items
from the class "LIKE" to be recommended to the user.
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
876
Figure 2: A general framework of our proposed CBF.
4 EXPERIMENT
4.1 Data Set
For our experiments we have used two data sets:
the Amazon Fine Food and Book review. The
two data sets consist of product and user infor-
mation, ratings, and a short text review (McAuley
and Leskovec, 2013; He and McAuley, 2016).
The data sets used in our experiments are ob-
tained from Stanford University’s Snap Dataset,
(https://snap.stanford.edu/data/web-Amazon.html).
The Amazon Fine Food data consists of 568,454
reviews between October 1999 and October 2012;
256,059 users and 74,258 items. The Amazon book
data set consists of 12,886,488 reviews; 2,588,991
users and 929,264 items. The both datasets contain 8
fields: product ID, user Id, profile name, helpfulness,
rating or score, time, product summary and text
review. The following example is one of user’s
review of dog food:
product/productId: B001E4KFG0
review/userId: A3SGXH7AUHU8GW
review/profileName: delmartian
review/helpfulness: 1/1
review/score: 5.0
review/time: 1303862400
review/summary: Good Quality Dog Food
review/text: "I have bought several of the Vitality
canned dog food products and have found them all
to be of good quality. The product looks more like a
stew than a processed meat and it smells better. My
Labrador is finicky and she appreciates this product
better than most".
In our experiments we have used only ve at-
tributes: <UserId, ProductId, Score/Rating, Sum-
mary, review>. The rating value is given by an integer
number between 1 and 5. To build the learner profile
we consider rating greater to 3 as the user likes the
item otherwise the user dislike it. For the both data
sets, we have selected only users that have more than
60 reviews. In total we have used about 70 users for
each data set.
4.2 Evaluation and Metrics
In our experiments, we have used the 5-fold cross val-
idation technique. For each user, the items already
reviewed and rated randomly split in 5 folds. One
of the fold was used for testing and the 4 remaining
folds were used for training. The training set was
used for building the prototypes of each class. The
experiments were executed ve times and the aver-
age values of the evaluation metrics were reported.
The following evaluation metrics were used to eval-
uate the CBF approaches: precision, recall and F1-
measure. These metrics are well-known in informa-
tion retrieval and widely used in content based filter-
ing to measure the effectiveness of CBF recommenda-
tion (Castro et al., 2014). Precision is the ratio of the
recommended items that are actually liked by users
to recommended items. Recall is the ratio of the rec-
ommended items that are actually liked by user to the
total number of items that user liked in user’s test data
set. F1 measure the trade-off between precision and
recall (F1 = 2 ×
precision×recall
precision+recall
).
4.3 Results & Discussion
A comparative analysis among the two models VSM
based k-NN and VSM based k-CR previously detailed
has been carried out by using the precision, recall and
F1-score, according to the number of recommenda-
tion that the CBF provides to the user. To test our re-
The k Closest Resemblance Classifier for Amazon Products Recommender System
877
sults we have used the Precision-Recall (PR) curves
for all customers. These curves show the relation
between the precision and recall of the two recom-
menders based CBF. In Fig. 3 and Fig. 4 the PR
curves for VSM based k-NN and VSM based k-CR
on respectively Amazon Fine Food and Amazon book
data sets are plotted.
Figure 3: Precision Recall curves for the CBF based k-NN
and CBF based k-CR on Amazon Fine Foods data.
As shown in fig. 3, the CBF based k-CR outper-
forms significantly CBF based k-NN on Amazon Fine
Food. The CBF based k-CR has higher average pre-
cision for a corresponding recall than CBF based k-
NN. If we consider the average recall of 40%, the
CBF based k-CR has average precision about 70%,
whereas the average precision of the CBF based k-NN
is about 45%. As shown in fig. 3 the CBF based k-CR
maintains higher precision than k-NN when the recall
has values 20% or higher and has almost the same
precision for recall value less than 20%. The same
results for the recall, the k-CR has higher recall for a
corresponding precision. For example, if we consider
a precision of 70%, the CBF based k-CR has an av-
erage recall about 40%, whereas the average recall of
CBF based k-NN is about 32%.
In the case of Amazon book data set the results are
not obvious and there is no clear difference between
the performances of k-NN and k-CR. As shown in fig.
4, when the average recall is set between 25% and
35%, the CBF based k-CR outperforms slightly the
CBF based k-NN. However, for the average recall val-
ues greater than 0.38% the CBF based k-NN is better
than k-CR.
The reason why the Amazon book data is less ob-
vious than other data set may lie to the difference be-
tween the both data sets used in our experiments. For
example the most customers selected in our experi-
ments in the book data sets have 100 % positive re-
views, which will have problem to build user profile
Figure 4: Precision Recall curves for the CBF based k-NN
and CBF based k-CR on Amazon books data.
for the class C
2
representing the items that the user
does not like.
Since the precision and recall curves do not give
more detail results such as how many times the rec-
ommender A is better than recommender B according
to the average precision per user. Hence, the results of
the both recommenders are divided into three classes:
better, equal and worse performances as presented in
Fig. 5 and Fig. 6.
In the Fig. 5 and Fig. 6 all users’ measurements
are sorted by review numbers. Dark color represents
better and lighter represents worse performance ac-
cording to precision. As shown in both figures the
CBF based k-CR yield better results than CBF based
k-NN. For the Amazon Food data 71% of customers
the k-CR has better precision than k-NN and only in
11% of users k-NN is better than k-CR. On the other
hand, in 51% of users the k-CR has better precision
than k-NN and only in 27%, it is better than k-CR. In
this work, to learn k-CR classifier we have considered
only one profile per class. We think by adding more
profiles per class we can improve the performances
regarding the precision as well as the recall.
5 CONCLUSIONS
In this work we have applied learning user models
based on the classifier k-CR for CBF using only the
short descriptions and the reviews of the products.
The system can recommend products to user based
only on the content of his past reviews with the re-
views of other customers on the products in Amazon
database. Currently, we are working in improving our
CBF by incorporating other features and by combin-
ing k-CR with other machine learning like deep learn-
ing and support vector machine to solve very large
ICAART 2020 - 12th International Conference on Agents and Artificial Intelligence
878
Figure 5: A comparison results for all users for food data. Red color represents the k-CR performances whereas the grey color
represents the k-NN results. The lighter the color, the worse is the results of k-CR. The pre-avg in the graph presenting the
average precision for each user.
Figure 6: A comparison results for all users for book data. Red colors represent the k-CR performances whereas the grey
color represents the k-NN results. The lighter the color, the worse is the results of k-CR. The pre-avg in the graph presenting
the average precision for each user.
The k Closest Resemblance Classifier for Amazon Products Recommender System
879
data sets. We are also investigating if the proposed
models would perform well in various other recom-
mendation applications based on short text, e.g. twit-
ter, blogs and RSS feeds.
ACKNOWLEDGEMENTS
The authors thank the MITACS Globalink Internship
program for undergraduate students for funding the
internship of the second author.
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