=Paper=
{{Paper
|id=Vol-1580/49
|storemode=property
|title=Improving Neighborhood-Based Collaborative Filtering by a Heuristic Approach and an Adjusted Similarity Measure
|pdfUrl=https://ceur-ws.org/Vol-1580/id49.pdf
|volume=Vol-1580
|authors=Yasser El Madani El Alami,El Habib Nfaoui,Omar El Beqqali
|dblpUrl=https://dblp.org/rec/conf/bdca/AlamiNB15
}}
==Improving Neighborhood-Based Collaborative Filtering by a Heuristic Approach and an Adjusted Similarity Measure==
https://ceur-ws.org/Vol-1580/id49.pdf
Proceedings of the International Conference on Big Data, Cloud and Applications
Tetuan, Morocco, May 25 - 26, 2015
Improving Neighborhood-Based Collaborative
Filtering by A Heuristic Approach and An Adjusted
Similarity Measure
Yasser El Madani El Alami El Habib Nfaoui Omar El Beqqali
Computer science department of Computer science department of Computer science department of
FSDM FSDM FSDM
Sidi Mohammed Ben Abdellah Sidi Mohammed Ben Abdellah Sidi Mohammed Ben Abdellah
University University University
Fez, Morocco Fez, Morocco Fez, Morocco
yasser.elmadanielalami@usmba.ac. elhabib.nfaoui@usmba.ac.ma omar.elbeqqali@usmba.ac.ma
ma
Abstract— “Collaborative filtering” is the most used approach in researches such as MovieLens [5] [6], they are being extended
recommendation systems since it provides good predictions. to other domains such as digital libraries [7], e-learning [8], etc.
However, it still suffers from many drawbacks such as sparsity
and scalability problems especially for huge datasets which Authors in [3] show that there are three main categories of
consist of a large number of users and items. This paper presents information filtering: content-based recommendations [9],
a new algorithm for neighborhood selection based on two collaborative filtering and hybrid recommendations where
heuristic approaches. The first of which is based on selecting collaborative filtering methods are the most used in
users who rated the same items as the active user called recommender systems [10]. They rely on users’ evaluation
“intersection neighborhood” while the second one builds the (ratings) to identify “useful” items to these users.
neighborhood using all users who rated one item at least as the Unfortunately, many typical drawbacks are noticed in
active user called “union neighborhood”. In addition, we employ collaborative filtering approaches which weaken thereafter the
an adjusted similarity measure that combines Pearson quality of the recommendations such as sparsity and scalability
correlation with a set-similarity measure (such as Jaccard problems.
similarity) as a correction coefficient for .accurate similarities
among users. Finally, experiments using FilmTrust dataset show Our work relies on using a heuristic approach in a
that the proposed approaches give more predictions accuracy preprocessing step for building users’ neighborhood which
than the traditional collaborative filtering. relies on the well-known set operators; union and intersection.
They induce a new ratings matrix with low dimension. This
Keywords—Collaborative filtering; Neighborhood ratings matrix is less sparse than the ratings matrix of the whole
selection;Spatial complexity; Recommender system; Similarity users. Therefore, this method leads to a minimum of time
measure consumption in the selection neighborhood phase. In parallel,
we use a reformed similarity measure that combines the well-
1 INTRODUCTION
known Pearson correlation with set-similarity measures as
Ever since the 90s, the amount of information has been adjustment coefficient which yields good results.
increased in exponential way. The Internet has played a key
role in information growth. Mobile devices such as smart This paper is organized as follows: in section 2 we give an
phones and tablets also contribute to this continuous expansion overview of the traditional collaborative filtering. In section 3
of information plethora. Thus, users are continually faced with we mention some recent works conducted in collaborative
information overload. It becomes difficult for them to filtering field. Section 4 describes our proposals. In section 5,
distinguish relevant information from noise. In order to address we present the experiments and evaluation results of our
this problem, there have been a great interest in proposals. At the end, we give some perspectives, and a
recommendation. According to [1] recommendation systems conclusion.
have been considered as an effective means to reduce 2 BACKGROUND
complexity in information retrieval. They promise to
personalize the request based on the user’s interest in a smart The term of collaborative filtering (CF) was introduced by
way [2]. As stated by [3] recommendation system helps users David Goldberg in [11] where he proposed a mail system
to deal with information overload and provides personalized called Tapestry that filters documents based on users’ interest
recommendations, content and services to them. It suggests the in order to be used by other people. Collaborative filtering is
appropriate items for each user according to his/her interests. based on mutual aid of users who share similar tastes and
Although, recommendation systems are largely used in both e- preferences to recommend the suitable items. According to
commerce applications such as Amazon [4] and academic [12], collaborative filtering relies on the following assumption:
if users X and Y rate n items similarly or have similar
1
16
behaviors, then, in the future, they will act (rating or behavior) Default rating: to set a same value for all unrated items.
on other cases similarly. As a result, CF based systems can
predict a user’s rating (or behavior) for an unknown item [11] Pre-processing using average: to set an average rating
or create a top-N list of recommended items for a target user based on user’s votes for the missing rating-matrix
(called active user) [13]. It is worth noting that the first work entry ̅
using CF was presented by Malone in [14] where he proposed 2.1.1.2 Neighborhood formation
stereotypes to build user models and use them to recommend
The second step consists of measuring similarity between
relevant books to each user.
the other users. They are several similarity algorithms such as
According to [15], we can distinguish between classes of Pearson correlation, mean-squared difference [22] and
CF algorithms: memory-based algorithm and model-based Spearman correlation [23]. The most commonly used algorithm
algorithms. In what follows we focus on the memory based is the Pearson correlation. In fact, it has become a standard way
approach of calculating correlation [19]. Using Pearson correlation,
similarity between user ua and ub is calculated with the
2.1 Memory based algorithm following formula:
Memory based approach builds predictions based on the whole
∑ ( )( )
set of ratings that users assigned to items before. Previous , = (1)
ratings are grouped in a matrix referred to as ratings matrix. It’s ∑ ( ) ∑ ( )
the pillar input in this approach. It was the earliest approach
adopted by many commercial systems thanks to its easiness where n is the cardinal of the set of items, rai is the rating given
and effectiveness [16]. by user a to item j and is the average rating given by user a
for all the items he rated. As an output, similarity process
Table 1 Example of ratings matrix
returns a user similarity matrix which determines correlation
between pairs of users. Thus, building similarity between users
Items Item 1 … Item j … Item n allows forming the requested neighborhood. Two techniques
Users have been employed [24]:
User 1 1 2 2 3
Threshold-based: user is considered as neighbor when
… 2 1 his/her user similarity exceeds a given threshold [25].
User s 1 ? K nearest users where k is given as input. Also, it can
… 3 3 5 be computed for each user as proposed in [26].
User p 1 4 2.1.1.3 Recommendation generation
This phase relies on generating predicted rating of user s to
As presented in table 1 above, also called ratings matrix, the item i. It’s calculated as aggregation of similarity between
cell rsj refers to the rating given by user s to item j (on 1-5 the active user and his neighborhood. It relies on both
rating scale). In most cases, this ratings’ matrix is typically ratings matrix (input) and similarity matrix.
sparse [17] as most users do not rate viewed items regularly.
Therefore, [18] argued that the sparsity can be an issue that can , = ∈ , (2)
lead to weak recommendations. Besides, the most popular
algorithm in memory based is neighbor-based algorithm which Various aggregation functions are employed in predictions
predicts ratings based on either users who are similar to the where the most used one is calculated as the weighted
active user or similar items to the requested item. Generally, average of neighbors’ ratings using their similarities as
According to [19] there are three steps into processing a follows:
recommendation based on CF system: i) Representation, ii) ∑ ( , )
Neighborhood formation, iii) Recommendation generation. , = ̅ + ∑ (3)
,
Neighbor-based approach is mainly divided in two analogous K represents the size of selected neighborhood.
categories: users-based collaborative filtering [15] and item-
based collaborative filtering [20]. For example, in what Therefore, based on computed predictions, recommender
follows, we detail the User-Based CF. system may select the top-N items as the recommendations list
of unknown or new items that the active user has never seen
2.1.1 User-based CF (UBCF) before.
User based recommendation relies on users similarity to the
active user. In fact, it builds prediction and recommendation 2.2 Performance measures
using the correlation between the active user and each other. Performance measures are the result of a step of monitoring a
proposed method or algorithm in real situation or
2.1.1.1 Representation approximating the reality with reliable data. In
The first step in UBCF consists on building a rating matrix recommendation system, a great research effort has been made
and assigning values to the unrated items to fill the porous to deal with this influential task such as presented in [27]. In
ratings matrix. Two processes [21] can overcome sparsity and our case we are interested by prediction performance measures.
improve recommendation accuracy:
2
17
In this area, many indicators are used to measure the system similarity measure. In the literature, almost all works are based
performance. In most cases, they tend to evaluate the system on the well-known Pearson correlation measure. As presented
accuracy. They measure the precision of computed predictions in formula (2), Pearson correlation measure doesn’t take into
comparing to the real user ratings. A case in point is Mean account other decisive which provide meaningful information
Absolute Error (MAE). In fact, MAE is a common way used to of how users’ preferences are different. Sparsity is another
measure accuracy based on statistical metric. It calculates the consistent problem which contributes to generating incorrect
average absolute difference between predicted ratings and real recommendations. In fact, only a small number of the whole
ones: items are rated then the matrix rating becomes sparse. In
∑( , )
addition, using a huge dataset required more time for
, ,
= (4) computing similarities among users in order to build an
effective neighborhood for the active user. Consequently,
In the formula above , is the predicted rating for user s to combining these factors increases mainly the margin of error
item i, , is the real rating given by user s to item i and N and reduces the confidence interval which leads to inaccurate
corresponds to the number of predicted ratings calculated recommendations.
during the test phase.
4 PROPOSED APPROACH
In [27] authors argue that using different evaluation metrics
leads to different conclusion concerning the recommendation In order to limit the problems of sparsity and time computing
system performance. Thus, most researches choose a single problems, we propose a preprocessing step which relies on a
evaluation measure in order to demonstrate the effectiveness of heuristic approach of neighborhood selection (figure 1).
their algorithms.
After giving a detailed overview of the background, and
presenting the broad outlines of collaborative filtering, in the
next section, we present some recent works and trends done in
this area in order to overcome drawbacks and illnesses of the
traditional approach.
3 RECENT WORKS
In the last decade, collaborative filtering approach
motivated a larger number of works adding in each one an
original concept and then opening a new perspective in order to
deal with the problem of recommendation. Clustering method
is one of the extensively used concepts in CF. One of the Figure 1 the proposed process of collaborative filtering
earliest works that have been done in this area was presented in
[28] where the authors argued that clustering improves the It can be done with two methods based on natural operators’
performance of recommendation. [29] Proposes to group users’ sets without computing similarities among users. The first
profiles into clusters of similar items and compose the method is the intersection selection. It’s based on selecting
recommendation list of items that match well with each cluster. users who rate the same item as the active user. The second one
Authors in [30] develop Eigentaste 5.0 recommender system is based on the union operator. It’s based on building the
that dynamically arranges the order of recommended items by neighborhood with users who rated one item in common at
integrating user clustering with item clustering. In [31] the least. Obviously, time computing is reduced because of the
author proposes a new probabilistic neighborhood-based reduction of the number of computed users’ similarities. In fact
approach as an improvement of the standard k-nearest neighbor our approach focuses on selecting neighbors who are likely to
algorithm. It’s based on classical metrics of dispersion and be reliable to the active user before starting similarities
diversity as well as on some newly proposed metrics. The computation phase which is time-consuming if we compute the
author also proposes the concept of unexpectedness in similarities for all system’s users. As a result, the new ratings
recommender systems. He also fully uses it by suggesting matrix is smaller than the ratings matrix of the system used in
the traditional collaborative filtering methods, which leads to
various mechanisms for specifying the expectations of the
less sparseness in the matrix.
users. Moreover, he proposes a recommendation method for
providing the users with non-obvious but high quality A. Intersection neighborhood
personalized recommendations that fairly match their interests.
This method is based on specific metrics of unexpectedness. In We call the first method of neighborhood selection the
[32] the authors propose an adapted normalization technique intersection neighborhood. It relies on selecting neighbors who
called mutual proximity in the nearest neighbor selection phase rate the same items. Actually, it’s rare to find two users who
to rescale the similarity space and symmetrize the nearest rate the same items. So, in order to deal with this point, we use
neighbor relation. They prove that removing hubs and a threshold of acceptance which corresponds to a minimum
incorporating normalized similarity values into the neighbor number of co-rated items. In what follows, we present the
weighting step leads to increased rating prediction accuracy. adopted algorithm:
One of the major factors in collaborative filtering that 1. For each active user.
greatly influences the recommendation accuracy is the selected
3
18
2. Extract the list Ia of items that the active user Ua rated 4. Select the target item whose rating value is going to be
before. Ia={i1, i2…,ip} and card(Ia)=p. predicted. For instance, as shown in the example below
(figure 5) we select the item I6.
3. Select users who rated the same list of items Ia as presented
in the figure below (figure 2) or having more than the
fixed threshold noted “TA”.
4. Select the target item whose rating value is going to be
predicted. As shown in the example below, we select the
item I6.
Figure 4 Basic ratings matrix
As presented in this example (figure 5), the neighborhood
contains two users U1 and U3. The new ratings matrix is:
Figure 2 Basic ratings matrix
As presented in this example (figure 3), the neighborhood
contains two users U1 and U3and the new adopted ratings
matrix is:
Figure 5 Derived ratings matrix
5. Fill up the empty cells with the average of ratings of each
user. As a direct result, the new ratings matrix contains
more users than the intersection algorithm but more empty
cells.
6. Compute the similarity between the selected users and the
Figure 3 Derived ratings matrix active user.
7. Select the top n similar users N={u1, u2, … un}
5. Fill up the empty cells with the average of ratings of each
user. As we can see, the new ratings matrix contains less 8. Generate the recommendation based on the selected
empty cells than the first one. neighborhood.
6. Compute the similarity between the selected users and the C. Similarity measure
active user using the new matrix of ratings.
In order to reduce the impact of the fallacious similarity on
7. Select the top n similar user N={u1, u2, … un} the computed recommendations, we propose to a modified
version of Pearson correlation. In [33], authors argue that
8. Generate the recommendation based on the selected adding associated parameters of users x and y improve the
neighborhood. similarity accuracy which lead to good predictions. Thus,
B. Union neighborhood the new similarity measure is presented as follows:
The second method of neighborhood selection is called the =S ∗ (5)
union neighborhood. . It relies on selecting neighbors who rate
one common item at least. The adopted algorithm is presented Where Corrp represents the traditional Pearson correlation
in what follows: and S represents Jaccard coefficient used as adjustment
coefficient:
1. For each active user
S = (6)
2. We extract the list Ia of items that the active user Ua rated
before. Ia={i1, i2…,ip} and card(Ia)=p where
3. Select users who rated one item of the list Ia at least as a=|X∩Y| represents the number of attributes (the rated
presented in the figure 4 below. items) which are present in user X and user Y.
b=|X-Y| represents the number of attributes (the rated
items) which are present in user X and not in user Y.
4
19
c=|Y-X| represents the number of attributes (the rated e. Repeat step d until predicting all the missing
items) which are present in user Y and not in user X. values
5 EXPERIMENTS AND RESULT f. Computing the MAE for each neighborhood
size by comparing the predicted values with
5.1 FilmTrust DataSet the observed ones.
For experiments we use the FilmTrust project dataset [34]. 4. Repeating this computation (from step 2) three times
It is an academic research project being run by Jennifer and then we give the average of the MAE for each user
Golbeck1. It’s a movie recommendation website where and neighborhood size.
users can rate and review movies. Users can give their
opinion using a quantitative value on a rating scale from 5.3 Results and analysis
0.5 to 4 stars where 0.5 means bad and 4 means excellent. 5.3.1 Experiment 1
The data is stored as semantic web annotations based on We start our test with comparing the traditional
RDF2 and FOAF3. It integrates semantic web-based on collaborative approach method (TCFM) with the union
social networks with movie ratings so as to compute neighborhood selection method (UNSM) by following the
predictive movie recommendations. The collected dataset steps presented before. Even though the variation behavior
consists of 1856 users, 2092 movies and 759922 ratings. of the union method is not regular, we can see (figure 6)
Thus, around 80.4% of the global ratings matrix is empty. that the union neighborhood selection method provides
It means that FilmTrust dataset represents a real situation good results since the computed MAE is less than the
of sparsity problem. traditional collaborative filtering one.
5.2 Experiments steps
MAE Union Neighborhood Selection
Our test consists of two main experiments. The first
Traditionnal collaborative Filtering
experiment focuses on comparing the traditional approach
0,78
with the union neighborhood selection method using the
0,77
adjustment coefficient presented before. The second
0,76
experiment focuses on comparing the comparing the
0,75
traditional approach with intersection neighborhood
0,74
selection method using the same adjustment coefficient.
0,73
The experiments respect the steps below: 0,72
0,71
1. From the dataset DS, for each user we build our ratings
0,7
matrix based on the definition presented below.
0,69
2. We randomly select 30% of the ratings and set the 0,68
value of those ratings to POSITIVE_INFINITY. In 0,67
fact, we use this value to distinguish between the 0,66
empty cells of the global ratings matrix which is 0,65
represented by null value and the modified ones. 0,64
Therefore we build two sets: 10 20 30 40 50 60 70 80 90
Set TG represents the training set (70% of the whole Neighborhood size
dataset) and Set T which represents the set of test with:
T=DS-TG
Figure 6 MAE comparison between TCFM and UNSM
3. For each user from the set DS.
a. We select users according to the employed 5.3.2 Experiment 2
method (intersection or union) The second experiment compares the traditional
collaborative filtering method (TCFM) with the
b. We build the rating matrix TG by filling up intersection neighborhood selection method (INSM) by
the missing ratings with the average ratings of following the steps outlined beforehand. In addition, in
each user. order to take user y as a candidate neighbor of the active
user x in intersection approach, we set 10 as a threshold
c. Use different size of neighborhood from 10
of co-rated items. Then, user ‘y’ will be selected if
users to 100.
he/she has10 co-rated items at least.
d. Employ the prediction formula to predict the
missing values.
1
https://www.cs.umd.edu/~golbeck/
2
http://www.w3.org/RDF/
3
http://www.foaf-project.org/original-intro
5
20
MAE Traditional Collaborative Filtering
As a result of these neighborhood selection methods, we
Intersection Neigborhood Selection
reduce the rating-matrix dimension which leads, on one
0,78 hand, to less sparseness in the induced matrix, and, on the
0,77 other hand, to minimizing the consumed time in
0,76 neighborhood selection phase. In addition, the prediction
0,75
accuracy is improved.
0,74 Despite this, the two approaches are not efficient for cold
0,73 start problem especially for intersection approach which
0,72 needs a threshold of ratings to provide good
0,71 recommendations. As future work, we will investigate
0,70 incorporating social network data in order to deal with this
0,69 problem. In fact, social networks offer many opportunities
0,68 for recommendations since people generally use their social
0,67 networks to obtain reliable and useful information.
0,66
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