International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 6, December 2021, pp. 5541~5548
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp5541-5548
5541
Recommendation system using the k-nearest neighbors and
singular value decomposition algorithms
Badr Hssina1, Abdelakder Grota2, Mohammed Erritali3
1Faculty
of Sciences and Technics, LIM Laboratory, Advanced Smart Systems (ASS) Hassan II University of
Casablanca, Morocco
2,3Faculty of Sciences and Technics, TIAD Laboratory, Computer Sciences Department, University of Sultan My
Slimane, Beni-Mellal, Morocco
Article Info
ABSTRACT
Article history:
Nowadays, recommendation systems are used successfully to provide items
(example: movies, music, books, news, images) tailored to user preferences.
Amongst the approaches existing to recommend adequate content, we use the
collaborative filtering approach of finding the information that satisfies the
user by using the reviews of other users. These reviews are stored in matrices
that their sizes increase exponentially to predict whether an item is relevant
or not. The evaluation shows that these systems provide unsatisfactory
recommendations because of what we call the cold start factor. Our objective
is to apply a hybrid approach to improve the quality of our recommendation
system. The benefit of this approach is the fact that it does not require a new
algorithm for calculating the predictions. We are going to apply two
algorithms: k-nearest neighbours (KNN) and the matrix factorization
algorithm of collaborative filtering which are based on the method of
(singular-value-decomposition). Our combined model has a very high
precision and the experiments show that our method can achieve better
results.
Received Nov 6, 2020
Revised May 26, 2021
Accepted Jun 12, 2021
Keywords:
Collaborative filtering
KNN
Matrix factorization items
Recommendation system
SVD
This is an open access article under the CC BY-SA license.
Corresponding Author:
Badr Hssina
Department of Computer Science
LIM Laboratory, Advanced Smart Systems (ASS)
Faculty of Sciences and Technics
Hassan II University of Casablanca
B.P. 146 Mohammedia-Morocco
Email: badr.hssina@fstm.ac.ma
1.
INTRODUCTION
Today, the reason people might be interested in using a recommender system is because they have
so many items to choose from in a limited period of time and they cannot rate all possible items. In general,
to achieve this goal, users must provide their own profile and they will receive a reduced and personalized
image of this information. At first glance, they look like news search engines. However, the difference is that
search engines allow you to return all items that match the query, ordered by degree of relevance. Whereas,
the goal of the recommendation is to return personalized, interesting and useful content to users.
Recommendation systems are a specific form of information filtering aimed at presenting items of
information (movies, music, books, news, images, and web pages) that are likely to be of interest to the user.
Generally, a recommendation system makes it possible to compare a user's profile with certain benchmark
characteristics, and seeks to predict his need [1]. Indeed, with the increase in the number of users on the
Journal homepage: http://ijece.iaescore.com
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Internet and the volume of data produced each day, it has become necessary to design techniques allowing
users to access what interests them as quickly as possible. Among the main problems of recommendation
systems is the problem of stability with respect to the dynamic profile of the user. This limitation comes from
the fact that if the user is interested in several different elements at the same time, as he can alternate his
preferences over time, and if his profile is created in the system, it becomes complex to change his
preferences and to take into account their different choices [2].
The capacity of recommendation systems remains limited to adapt to the different choices and
preferences of users and to follow the evolution of their profiles, as well as to recommend elements that do
not correspond to their different choices and interests, which brings us back to a lack of diversity in the list of
recommendations [2]. Despite this, recently good recommendation systems with new qualities have been
presented in the literature [3]. The quality of a recommendation system can be seen in its effectiveness in
providing users with new and diverse articles, which meet their different interests and preferences.
To answer the problem of the stability of recommendation systems and to offer diversified
recommendations in relation to the dynamic profile of users and the scalability of the data, we present in this
work a recommendation system capable of generating diversified recommendations [2], [3]. These types of
systems respond to different user requests and interests, by developing recommendation algorithms allowing
users to belong to different groups, with similar interests, nearest neighbors are selected using a new metric
of similarity based on the difference in presence between the membership degrees of the user asset and
similar members and a matrix factorization method [4].
2.
RELATED WORK
Recommendation systems are a specific form of information filtering aimed at presenting items of
information (movies, music, books, news, images, and web pages) that are likely to be of interest to the user.
Generally, a recommendation system makes it possible to compare a user's profile with certain benchmark
characteristics, and seeks to predict his need. The most popular definition of recommendation systems is that
of Robin Burke: A system capable of providing personalized recommendations or guiding the user to
relevant or useful resources within a large data space [4]. The repository of a recommendation system usually
consists of a list of users who have expressed their preferences for various items. As mentioned before, a
choice expressed by a user for an item is called a view, and is often represented by a triple (user, rating, and
item). These views can take different forms. Moreover, the majority of recommendation systems use binary
ratings (like/dislike) or scores in the form of a scale of 1 to 5. The triplets (user, element and rating) form this
called the score matrix. The pairs (element, user) for which the user did not give an element score are values
ignored in the matrix.
The objective of a referral system can be summed up in two parts. The first part is prediction: given
a user and an item, what would be the user's preference for that item, the system must predict the value of the
marked notes. The second part is the recommendation; what ordered list (n elements) of recommendations
can the system suggest? This is called a Top-n list. It should be mentioned that the list of n recommendations
is not necessarily the list of n elements with the most relevant prediction values. A recommendation
algorithm can use other criteria, such as context [3], [4] because score prediction is not the only criterion used
to produce a list of recommendations [5].
3. PHASES OF RECOMMANDATION PROCESS OF OUR SYSTEM
3.1. Information collection
The two major concepts to take into account in the data collection phase are: data sources and data
producers. On the one hand, the concept of data source poses problems linked to the multiplicity and
reliability of data sources. On the other hand, the concept of data producer raises several issues, the most
important of which is the actual choice of persons from whom the user profile can be derived [4].
Furthermore, for the data preprocessing phase, existing conventional techniques can be implemented: data
reduction, data transformation, data transmoding and data transcoding. The data that will be used by the data
mining algorithms in the data preparation phase can be ideally structured according to 4 concepts [5]:
−
Explicit data (provided explicitly by the user and which can be reused by the mechanisms for using the profile);
−
Implicit data (implicitly collected from user interactions) which will make it possible to define
indicators allowing user preferences to be deduced;
−
Context data for better use according to the contexts of the profiles constructed;
−
Semantic data which is based on semantic resources to remove semantic ambiguities on the implicit
data used to build the profiles.
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3.2. Explicit feedback
In this type of feedback, users are forced to give their opinion on products, objects or items in general.
Users can do this via a rating system (for example with 5 stars to validate, a satisfaction questionnaire), or by
posting their opinion on a given element (for example the “Like” function on social networks) [6].
3.3. Implicit feedback
The implicit collection, also called passive, concerns the interactions of users on the system. This
collection includes the number of views on a video, the tracking of the number of visits to a page, the history
of purchases on an e-commerce platform or the time spent on a given section [7].
4. RECOMMANDATION SYSTEM APPROCHES
4.1. Content-based filtering
Content filtering is based on the content of documents (subjects) to compare them to a profile, itself
made up of subjects. This type of filtering is a general evolution of studies on information filtering. System
users then have a profile that describes their areas of interest. Each user's profile can contain a list of topics or
preferences [8], [9]. The system compares the description of a new arrived document with the user's profile to
predict the usefulness of that document for that user [10]. Associating documents with a user profile is an
advantage of content-based filtering systems.
4.2. Collaborative filtering
The objective of collaborative filtering [11] is to use the evaluations made by users on certain
documents (content), in order to recommend these same documents to other users, and without analyzing the
content of the documents. Users of a collaborative filtering system can benefit from the results of others by
receiving recommendations for which closest users have given a favorable value judgment, without the
system extracting the content of the documents. This independence of the system in relation to the
representation of the data [11], [12] can be applied in contexts where the content is difficult to analyze, and in
particular it can be used for any type of data: text, audio, image and video. Thus, the user is able to discover
different interesting areas, because the principle of collaborative filtering is absolutely not based on the
thematic dimension of profiles, and is not subject to the “funnel” effect [13]. Collaborative filtering
constitutes an important advantage which is that the value judgments of the users integrate not only the
thematic dimension but also other factors related to the quality of the documents such as novelty and
diversity. Among the problems with this type of filtering is cold start [14]: it is the fact that a user must vote
on a large number of items before getting recommendations.
4.2.1. Memory based techniques
The data is represented in the form of a "User x Item" matrix [15] for a collaborative memory-based
filtering system. The rows represent the users and the columns constitute the items. The type of memorybased approaches use user feedback on items (in the form of reviews), in order to generate recommendations.
This type mainly applies statistical techniques in order to identify neighboring users having, on the same set
of elements, ratings similar to those of the active user. Once the neighbors are identified, the memory-based
approach uses different algorithms to combine the opinions of the neighbors and generate predictions to the
active user [16]. This method mainly uses ranks.
The degree of correlation between the users and the user for whom we wish to make the
recommendation determines the weight given to the rating of each user. Since systems typically have to
handle a large number of users, then making recommendations based on ratings from millions of users can
have severe performance consequences [17], [18]. Furthermore, when the number of users reaches a certain
threshold, a selection of the “best” neighbors must be made. Pearson's similarity is based on the calculation
of correlations, only user currents are taken into account [19].
𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑠𝑠𝑠𝑠𝑠𝑠 (𝑢𝑢, 𝑣𝑣) =
∑𝑢𝑢∈𝐼𝐼𝑢𝑢𝑣𝑣(𝑟𝑟𝑢𝑢𝑢𝑢 −𝜇𝜇𝑢𝑢 ).(𝑟𝑟𝑣𝑣𝑢𝑢 −𝜇𝜇𝑣𝑣 )
�∑𝑢𝑢∈𝐼𝐼𝑢𝑢𝑣𝑣(𝑟𝑟𝑢𝑢𝑢𝑢 −𝜇𝜇𝑢𝑢 )2 �∑𝑢𝑢∈𝐼𝐼𝑢𝑢𝑣𝑣(𝑟𝑟𝑣𝑣𝑢𝑢 −𝜇𝜇𝑣𝑣 )2
Vector space models are widely adopted in the field of information retrieval, so we will talk about
numerical similarity [19]. These approaches use a feature vector, in dimensional space, to represent each
object and calculate numerical similarity based on the cosine measure or Pearson's correlation. Among the
approaches cited in the literature we can cite: This measurement uses the full vector representation, i.e. the
frequency of objects (words).
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Two objects (documents) are similar if their vectors are confused. If two objects are not similar,
their vectors form an angle (U, V) whose cosine represents the value of the similarity. The formula is defined
by the ratio of the dot product of the vectors u and v and the product of the norm of u and v.
cosinesim (𝑢𝑢, 𝑣𝑣) =
∑𝑢𝑢∈𝐼𝐼𝑢𝑢𝑣𝑣 𝑝𝑝𝑢𝑢𝑢𝑢 .𝑝𝑝𝑣𝑣𝑢𝑢
�∑𝑢𝑢∈𝐼𝐼𝑢𝑢𝑣𝑣 𝑝𝑝𝑢𝑢𝑢𝑢 2 .�∑𝑢𝑢∈𝐼𝐼𝑢𝑢𝑣𝑣 𝑝𝑝𝑣𝑣𝑢𝑢 2
We generally use the k-nearest neighbor (k-NN) algorithm to determine which are the most relevant
neighbors to select and generate reliable recommendations, which allows to select only the k best neighbors
with the highest correlation value. Can distinguish two methods of collaborative filtering based on memory:
the method based on item-centered memory and the user-centered memory-based method [20].
4.2.2. Model-based techniques
Models have been incorporated into recommendation systems for improve and remedy problems
with memory-based methods. Algorithms based on the model are also based on previous evaluations
(profiles) of users, but this method does not directly calculate predictions, it classifies users according to
groups or learn models from their data. For the construction of the model several methods are used. In
general, the methods based on the model use machine learning techniques, such as clustering, matrix
factorization, Bayesian networks, and decision trees [19], [20].
In our approach, we will focus mainly on matrix factorization as well called matrix de-composition.
It consists in breaking down a matrix into several other matrices. To find the original matrix, it will suffice to
make the product of these matrices between them. The Matrix factorization has given good results in
recommender systems.
5.
HYBRID FILTRING
Noting the advantages and disadvantages of each of the two above approaches, it is understood that
many systems are based on their combination, which makes them so-called hybrid filtering systems
[21]-[23]. In general, hybridization takes place in two phases as shown in Figure 1:
−
Separately apply collaborative filtering and other filtering techniques to generate candidate
recommendations.
−
Combine these sets of preliminary recommendations using some methods such as weighting, mixing,
cascading, and switching, to produce the final recommendations for users [9]. More generally, hybrid
systems manage content-oriented user profiles, and the comparison between these profiles results in the
formation of user communities that allow collaborative filtering [24].
Figure 1. Hybrid filtering techniques
6. EVALUATION METRICS FOR RECOMMENDATION ALGORITHMS
6.1. Dataset
In this work we will analyze the Movielens 100 k Dataset which consists of 100.000 ratings from
1000 users on 1700 movies. All users in this dataset have at least rated 20 movies. Apart from this
information, simple demographic information for the users like age, gender, occupation is included. The
dataset can be obtained on the following permalink: http://grouplens.org/datasets/movielens/100k/.
We will use a hybrid collaborative filtering approach where we will combine the results of the k
nearest neighbor algorithm and the model based SVD algorithm to predict the movie ratings of the users [25],
[26]. The advantage of the collaborative filtering algorithms is that no knowledge about item features is
needed. So we can ignore the movie tags and the demographic information and concentrate on the users and
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their ratings. We will evaluate the hybrid model to see if a combination between a model based (SVD) and a
memory-based (KNN) approach delivers better results than each of the approaches on their own.
6.2. Results and evaluation
For the implementation of this project we have used “surprise” a Python scikit for recommender
systems. It has predefined all major recommendation algorithms such as KNN, SVD. We created a new
hybrid algorithm by combining the results of KNN and SVD. On http://surpriselib.com/ you have access to
the surprise library.
Hence, we first run SVD on the training data and get a model. Then we do the same with KNN.
With KNN we implemented a user-based collaborative filtering model. To compute the similarity between
the K nearest neighbor in the KNN algorithm we used cosine similarity. For both SVD and KNN we get
predictions for the movie ratings of each user. The results are combined by averaging the estimated rating of
KNN and SVD.
We used 5 cross-fold validation for splitting our data in train and testing sets. As evaluation metrics
we used root mean square error, mean absolute error and precision and recall. The precision and recall results
of the 5 cross fold validation was averaged for each algorithm (SVD, KNN, combination of SVD and KNN,
random prediction).
𝑀𝑀𝑀𝑀𝑀𝑀 =
1
∑
� |𝑝𝑝
|𝑅𝑅� | 𝑟𝑟̂ 𝑢𝑢𝑢𝑢 ∈𝑅𝑅 𝑢𝑢𝑠𝑠
− 𝑝𝑝̂𝑢𝑢𝑠𝑠 |
Where 𝑝𝑝𝑢𝑢𝑠𝑠 is the predicted rating for user u on item i, 𝑝𝑝𝑢𝑢𝑠𝑠 is the actual rating and N is the total number of
ratings on the item set. The lower the MAE, the more accurately the recommendation engine predicts user
ratings. Also, the root mean square error (RMSE) is given by Cotter et al. [27] as;
1
𝑅𝑅𝑀𝑀𝑅𝑅𝑀𝑀 = � |𝑅𝑅�| ∑𝑟𝑟̂ 𝑢𝑢𝑢𝑢 ∈𝑅𝑅�(𝑝𝑝𝑢𝑢𝑠𝑠 − 𝑝𝑝̂𝑢𝑢𝑠𝑠 )2
Root mean square error (RMSE) puts more emphasis on larger absolute error and the lower the
RMSE is, the better the recommendation accuracy. As we can observe in Figures 2 and 3, the SVD model
outperforms KNN and the random predictor in all metrics. It has the smallest RMSE, MAE and recall and the
highest precision. The KNN model is nearly as good as SVD. SVD is just 3.95 % better in RMSE, 3.99%
better in MAE. Furthermore, SVD has a 3.94% higher precision and a 5.69 % better recall rate. Of course,
both, KNN and SVD, are much better than the random prediction model.
Figure 2. Evaluation of RMSE, MAE on 100k and on 1M of the two algorithms KNN and SVD
KNN for example has a 37.28% smaller MAE and a 36.14% smaller RMSE than the random
predictor, which is enormous. It should also be noted, that the difference for MAE and the difference for
RMSE between the models is almost the same. For example, SVD is around 4% better in RMSE (the exact
value is 3.95% as stated before) as well as in MAE (3.99%) than KNN. And KNN is around 36% better in
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RMSE and MAE than the random predictor. This closeness between RMSE and MAE may indicate, that
these metrics are very similar and that one does not get any additional information by applying both metrics.
Now let us compare our combined model with the other models. Since SVD is the best of the single
models, it is sufficient to just compare SVD with the combined model. In regard to RMSE, the combined
approach is only 0.245% better than the SVD model. For MAE however, it’s the opposite, here the SVD
model is 0.256% better than the combined approach. Regarding precision SVD has a 0.126% higher
precision that the combined model. Also the recall rate of the SVD algorithm is 0.7% higher than that of the
combined algorithm.
Figure 3. Evaluation of fit-time test-time on 100k and on 1M of the two algorithms KNN and SVD
7.
CONCLUSION
As a conclusion, our combined model has very high precision and experiments show better results.
This means that most of the rec-ommended items are relevant. Though, the model has also a relatively low
recall, which means that the proportion of relevant items that are recommended is very small. The same
applies for SVD and KNN. The results have shown, that the com-bined model, where we averaged the
estimated ratings of the KNN and SVD model, is not significantly better than for example the SVD model
alone. In fact, we can ob-serve from the results, that the SVD model performs much better than the KNN
model on the 100k movielens dataset, such that, if we combine the models, the result for the combined model
is in most metrics (MAE, precision and recall) slightly worse than for the SVD algorithm. Hence, the
combination of the SVD and KNN model is not worth the effort and we would do better if we just used the
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SVD algorithm. As a mod-el-based approach it is much faster than the KNN approach, because we have only
to generate the model the first time and then can use this for new data points. This approach potentially offers
the benefits of both speed and scalability.
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BIOGRAPHIES OF AUTHORS
Badr Hssina obtained a master's degree in business intelligence from the faculty of science and
Techniques, Beni Mellal at Morocco in 2011 and a Ph.D degree in Computer Sciences from the
same faculty of Sultan Moulay Slimane University, Morocco in 2017. His current interests
include developing specification and design techniques for use within E-learning, data mining,
information Retrieval system, semantic web and cryptography. He is currently a professor at the
Faculty of Science and Techniques, Mohammedia, University Hassan II of Casablanca, and also
a member of the LIM laboratory, and the team Advanced Smart Systems (ASS).
Abdelakder Grota obtained a master's degree in business intelligence from the faculty of
science and Techniques, Beni Mellal at Morocco in 2020, and Ph.D student in the TIAD
laboratory of sultan moulay slimane University.
Mohammed Erritali obtained a master's degree in business intelligence from the faculty of
science and Techniques, Beni Mellal at Morocco in 2010 and a Ph.D. degree in Computer
Sciences from the faculty of sciences, Mohamed V Agdal University, Rabat, Morocco in 2013.
His current interests include developing specification and design techniques for use within
Intelligent Network, data mining, information Retrieval, image processing and cryptography. He
is currently a professor at the Faculty of Science and Techniques, University Sultan Moulay
Slimane, and also a member of the TIAD laboratory.
Int J Elec & Comp Eng, Vol. 11, No. 6, December 2021 : 5541 - 5548