Active Learning and User Segmentation for the Cold-start Problem in

Recommendation Systems

Rabaa Alabdulrahman

, Herna Viktor

and Eric Paquet

1,2

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

National Research Council of Canada, Ottawa, Canada

Keywords: Recommendation Systems, Collaborative Filtering, Cold-start, Active Learning.

Abstract: Recommendation systems, which are employed to mitigate the information overload faced by e-commerce

users, have succeeded in aiding customers during their online shopping experience. However, to be able to

make accurate recommendations, these systems require information about the items for sale and information

about users’ individual preferences. Making recommendations to new customers, with no prior data in the

system, is therefore challenging. This scenario, called the “cold-start problem,” hinders the accuracy of

recommendations made to a new user. In this paper, we introduce the popular users personalized predictions

(PUPP) framework to address cold-starts. In this framework, soft clustering and active learning is used to

accurately recommend items to new users. Experimental evaluation shows that the PUPP framework results

in high performance and accurate predictions. Further, focusing on frequent, or so-called “popular,” users

during our active-learning stage clearly benefits the learning process.

1 INTRODUCTION

Investors and businesses are turning increasingly

toward online shopping to maximize their revenues.

However, with the rapid development in technology

and the increase in available online businesses, clients

are increasingly overwhelmed by the amount of

information to which they are submitted.

Recommendation systems were introduced to aid

customers in dealing with this vast amount of

information and guide them in making the right

purchasing decisions (Lu et al., 2015). Yet, a

persistent drawback is that these systems cannot

always provide a personalized or human touch (Kim

et al., 2017). Intuitively, when a business owner does

not directly, or verbally, interact with the customer,

he or she has to rely on the data collected from

previous purchases. In general, research has shown

that vendors are better at recognizing and segmenting

users (Kim et al., 2017) than existing

recommendation systems are. This observation holds

especially for new customers.

The primary purpose of recommendation systems

is to address the information overload users

experience and to aid the users in narrowing down

their purchase options. These systems aim to achieve

this by understanding their customers’ preferences

not only by recognizing the ratings they give for

specific items, but also by considering their social and

demographic information (Bhagat et al., 2014).

Consequently, these systems create a database for

both items and users where ratings and reviews of

these items are collected (Minkov et al., 2010).

Intuitively, the more information and ratings

collected about the user, the more accurate the

recommendations (Karimi et al., 2015).

Generally speaking, recommendation systems fall

primarily into three categories. These are content-

based filtering (CBF) (Tsai, 2016), collaborative

filtering (CF) (Liao and Lee, 2016), and hybrid

approaches (Ntoutsi et al., 2014). These systems rely

on two basic inputs: the set of users in the system, 

(also known as customers), and the set of items to be

rated by the users,  (also known as the products)

(Bakshi et al., 2014).

All these systems employ matrices based on past

purchase patterns. With CBF, the system focuses on

item matrices where it is assumed that if a user liked

an item in the past, he or she is more inclined to like

a similar item in the future (Minkov et al., 2010,

Acosta et al., 2014).These systems therefore study the

attributes of the items (Liao and Lee, 2016). On the

other hand, CF systems focus on user-rating matrices,

recommending items that have been rated by other

Alabdulrahman, R., Viktor, H. and Paquet, E.

Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems.

DOI: 10.5220/0008162901130123

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 113-123

ISBN: 978-989-758-382-7

113

users with preferences similar to those of the targeted

user (Saha et al., 2015). These systems therefore rely

on the historic data of user rating and similarities

across the user network (Minkov et al., 2010). Lastly,

hybrid systems employ both CBF and CF approaches.

These systems concurrently consider items based on

users’ preferences and on the similarity between the

items’ content (Acosta et al., 2014). In recent years,

research has trended toward hybrid systems (Liao and

Lee, 2016). Another growing trend is the use of data

mining and machine learning algorithms (Bajpai and

Yadav, 2018) to identify patterns in user’s interests

and behaviours (Bajpai and Yadav, 2018).

In this paper, we present the popular users

personalized predictions (PUPP) framework,

designed to address the cold-start problem. In our

framework, we combine cluster analysis and active

learning, or so-called “user-in-the-loop,” to assign

new customers to the most appropriate groups. We

create user segmentations via cluster analysis. Then,

as new users enter the system, classification methods

assign them to the right groups. Based on this

assignment, we apply active learning. Cluster

analysis is used to group similar user profiles, while

active learning is employed to learn the labels

associated with these groups.

The remainder of this paper is organized as

follows: Section 3 presents our PUPP framework and

its components; Section 4 discusses our experimental

setup and data preparation, and Section 5 discusses

the results. Section 6 concludes the paper.

2 RELATED WORK

In active learning, or “user in the loop,” a machine

learning algorithm selects the best data samples to

present to a domain expert for labelling. These

samples are then used to bootstrap the learning

process, in that these examples are subsequently used

in a supervised learning setting. In recommendation

systems, active learning presents a utility-based

approach to collect more information about the users

(Karimi et al., 2011). Intuitively, showing the user a

number of questions about their preferences, or

asking for more personal information such as age or

gender, may benefit the learning process (Wang et al.,

2017).

The literature addressing the cold-start problem

(Gope and Jain, 2017) is divided into implicit and

explicit approaches. On the implicit side, the system

utilizes existing information to create its

recommendations by adopting traditional filtering

strategies or by employing social network analysis.

For instance, Wang et al. relies on an implicit

approach based on questionnaires and active learning

to engage the users in a conversation aimed at

collecting additional preferences. Based on the

previously collected data, the user’s preferences and

predictions, the active-learning method is used to

determine the best questions to be asked (Wang et al.,

2017). Similarly, explicit standard approaches may be

extended by incorporating active-learning methods in

the data-collection phase (Gope and Jain, 2017). For

instance, Fernandez-Tobias et al. use an explicit

framework to compare three methods based on the

users’ personal information (Fernandez-Tobias et al.,

2016). First, they include the personal information to

improve a collaborative filtering framework

performance. Then they use active learning to further

improve the performance by adding more personal

information from existing domains. Finally, they

supplement the lack of preference data in the main

domain using users’ personal information from

supporting domains.

There are many examples in the literature of

machine learning techniques being utilized in

recommendation systems. Although hybrid filtering

was proposed as a solution to the limitations of CBF

and CF, hybrid filtering still does not adequately

address issues such as data sparsity, where the

number of items in the database is much larger than

the items a customer typically selects, and grey sheep,

which refers to atypical users. Further, a system may

still be affected when recommending items to new

users (cold starts). To this end, Pereira and Hruschka

(2015) proposed a simultaneous co-clustering and

learning (SCOAL) framework to deal with new users

and items. According to their data-mining

methodology, a cluster analysis approach is

integrated in the hybrid recommendation system,

which results in better recommendations (Pereira and

Hruschka, 2015).

In addition, performances may be improved by

implementing classification according to association

rule techniques (Lucas et al., 2012). Such a system

was built in order to deal with sparsity and scalability

in both CF and CBF approaches. In (Soundarya et al.,

2017), clustering and classifications are used to

identify criminal behavior. Also, Davoudi and

Chatterjee in (Davoudi and Chatterjee, 2017) use

clustering to recognize profile injection attacks. Both

methods utilize clustering techniques to create user

segmentations prior to classification.

In our PUPP framework, we extend this approach

when creating our user groups. Our PUPP framework

is presented in the next section.

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Algorithm 1: Popular user personalized prediction (PUPP).

Input

 a set of  class labelled

training inputs;





 Clustering algorithm;

 Number of clusters;





 ratings per user;

 class label of ;

 unkown sample;

User Segmentation

1- 



discover  objects from  as

initial cluster centre

2- Repeat:

- (re)assign each object to

cluster according to





distance measure

- Update 



- Calculate new value

Until no change

3- Output models 



  





Initialization for classification and

prediction:

1- Classify 



  ;

2- Output classification model

3- Test model on 





4- Output prediction list

Initialization for active user rating

stage:

1- Select 2 highest prediction rate

2- Return 2 highest 







3- Remove 







from 





Append 







to 





3 FRAMEWORK

Our PUPP framework for prediction-based

personalized active learning is tested for its ability to

address the cold-start problem using a clustering and

two classification algorithms. First, we use soft

clustering, the EM method, to create our user

segmentation. Then, we use k-NN (EM-k-NN

framework) and subspace method (EM-subspace

framework) with k-NN as a base classifier for the

clustered data set. The results from these two

frameworks are compared with the traditional CF

(using k-NN) framework, which constitutes our

baseline.

In active learning, the learner will query the

instances’ labels using different scenarios. In this

framework, we use pool-based sampling, wherein

instances are drawn from a pool of unlabelled data

(Elahi et al., 2016). These instances are selected by

focusing on the items with the highest prediction

rates, using explicit information extraction (Elahi et

al., 2014, Elahi et al., 2016). As mentioned above,

active learning is an effective way to collect more

information about the user. Hence, in this framework,

if a new user rates a small number of highly relevant

items, that may be sufficient for first analyzing the

items features and then calculating the similarity to

other items in the system.

3.1 Framework Components

Figure 1 shows the steps involved in the PUPP

framework. Initially, we employ cluster analysis to

assign customers to groups, using a soft clustering

approach (Mishra et al., 2015). This results in

overlapping clusters, where a user may belong to

more than one cluster. Intuitively, this approach

accurately reflects the human behavioural

complexity. Once the groups are created, we apply

two splitting methods to generate the training and test

sets. We use a random split method—a common

practice in machine learning. In addition, we designed

an approach that focusses on so-called “popular”

users, as detailed in section 4.3. The cold-start

problem is addressed as follows. When a new user

logs in to the system, the initial model is employed to

find user groups with similar preferences. In our

approach, we employ the k-nearest neighbour (k-NN)

algorithm to assign a new user to a given group

(Sridevi et al., 2016, Katarya and Verma, 2016). A

machine learning algorithm is used to evaluate and

potentially improve the group assignment. To this

end, a human expert evaluates the predictive outcome

and selects two records (for each user) with the

highest prediction rate. These are appended to the

training set (Flach, 2012, Elahi et al., 2014). Then, a

new model is trained against the new, enlarged data

set. This process is repeated until a stopping criterion

is met. The following two subsections will discuss

these steps in detail.

3.1.1 Cluster Analysis Component

Cluster analysis is an unsupervised learning

technique used to group data when class labels are

unknown (Flach, 2012). Cluster analysis allows for

determining the data distribution while discovering

patterns and natural groups (Pujari et al., 2001). In an

e-commerce setting, the goal is to maximize the

similarity of individuals within the group while

minimizing the similarity of characteristics between

groups (Cho et al., 2015). Therefore, similarities in

Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems

115

opinion, likes and ratings of the users are evaluated

for each group, (Isinkaye et al., 2015).

Numerous options for algorithm are available for

cluster analysis. With soft clustering, the groups may

overlap; as a result, a data point may belong to more

than one group. Intuitively, in recommendation

systems, users’ group memberships are often fuzzy.

In previous work done by (Alabdulrahman et al.,

2018), the authors compare the performance of

different clustering and classification techniques, and

concluded that expectation maximization (EM)

clustering outperforms the other algorithms in most

cases. We therefore use EM clustering in our PUPP

framework. The EM algorithm proceeds by re-

estimating the assigned probabilities, adjusting the

mean and variance values to improve the assignment

points, iterating until convergence (Bifet and Kirkby,

2009).

3.1.2 Classification Component

In contrast to clustering, with classification, also

called “supervised learning,” the system learns from

examples where the class labels are known, from

which it develops classification models that it uses to

predict unknown instances (Pujari et al., 2001). Since

our framework is based on a CF recommendation

system, the k-nearest neighbor (k-NN) classifier is

employed in the PUPP framework. The latter also acts

as a baseline in our experimental evaluations (Sridevi

et al., 2016, Katarya and Verma, 2016).

In addition, we use the random subspace

ensemble-based method, whose advantages have

been demonstrated in our earlier research

(Alabdulrahman et al., 2018). Specifically, ensemble

improves the classification accuracy of a single

classifier (Witten et al., 2016). Also, in the random

subspace method, the learning process will focus on

the features instead of the examples. Hence, this

approach will evaluate all features in the subspace

and select the most informative ones based on the

selected features. That is, feature subsets will be

created randomly with replacement from the training

set. Then each individual classifier will learn from the

created subsets while considering all training

examples (Sun, 2007).

4 EXPERIMENTAL SETUP

The experimental evaluation was conducted on a

desktop with an Intel i7 Core 2.7 GHz processor and

16 GB of RAM. Our framework was implemented

using the WEKA data-mining environment (Frank et

al., 2016).

4.1 Dataset Description

We used two data sets to evaluate our PUPP

framework. We tested our framework on the

Serendipity data set (Kotkov et al., 2018), which

contains 2,150 movie ratings, as well as descriptions

of the movies and users’ responses to questionnaires

about the movies they have rated.

The second data set used is the famous

MovieLense data set (Harper and Konstan, 2016).

This data set, which is well-known in

recommendation system research, contains 100,836

ratings on 9,742 movies.

Figure 1: Outline of the PUPP framework.

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4.2 Dataset Pre-processing

Initially, the movie genres were determined with the

help of statista.com and imdb.com, as shown in Table

Table 1: Genre Coding.

Genre

Code

Genre

Code

Adventure

Children

Action

Documentary

Drama

Sci-Fi

Comedy

Musical

Thriller (crime)

Animation

Horror

Others

Romantic Comedy - Romance

Additional preprocessing steps involved

removing all ratings lower than  out of 5 to focus

the recommendations on popular movies. Also, for

the Serendipity data set, attributes 



to  provide

information about survey answers. These answers

relate to users’ experience using the recommendation

system and of the movie suggestions presented to

them. If less than 5 questions were answered, the

record was removed for lack of information. In total,

we eliminated 18 records.

4.3 Experimental Setup

In this experimental evaluation, we employed the EM

cluster analysis algorithm to segment users into

potentially overlapping clusters. We utilized two

classifiers, namely k-NN and the random subspace

ensemble method with k-NN as the base learner. The

value of k was set to 5, while the number of features

to be included in a subspace was fixed at 0.50 (50%);

both values were set by inspection.

As described above, our PUPP framework

includes a prediction-based personalized active

learning component. In our implementation, active

learning consists of iterations, where in each iteration

we select, for each user, the two (2) records with the

highest prediction rate. After labelling, these two

records are appended to the original training set and

removed from the test set. In the present work, the

number of iterations is limited to five to process the

request in real time. Our model was evaluated using

the 10-fold cross validation approach.

4.4 Cold-start Simulation

This section explains the approach for simulating the

cold-start problem. We use two techniques to split our

data sets, random split and popularity split. Each

technique was evaluated against the traditional k-NN,

EM-k-NN, and EM-subspace.

In the random split method, the data set is divided

randomly between (70%) training and (30%) testing

sets, where the training data set contains the known

rating by the system, as already provided by the users.

The test set, on the other hand, includes unknown

ratings. Note that this approach is commonly taken in

the literature (Flach, 2012).

Popularity split evaluates the popularity

associated with the users and the items. In this

scenario, we consider the users with the highest

number of ratings and refer to them as “popular

users,” i.e. those who use the system frequently.

These users are removed from the training set and

used as test subjects for cold-start simulations. A

“removed” user must have rated at least 5 popular

movies to be considered for removal, 5 movies was

determined by inspection. By removing members in

this manner, we increase the chance for the system to

find similarities among more users’ segmentations in

the system. This is, as far as we are aware, the first

research to use the notion of popular, or frequent,

users for guiding the determination of the

recommendations made to cold starts. We do so based

on the assumption of trends (such as in clothing

recommendation systems) and top rating systems for

movies or music (such as in Netflix and iTunes).

For a user to be considered as a test subject, the

following criteria must be met:

The user must have a high number of ratings, as

opposed to random split, where the number of items

rated by the user is ignored, as shown in Table 8.

The rated movies must have a rating greater than

(out of 5).

User rated popular movies. The non-popular

movie creates a grey sheep problem, which refers to

users who are atypical. We do not address grey sheep

in the present work.

We illustrate our results with 10 users. Table 8

shows some information about the selected users in

the MovieLense data set. It is important to stress that

we need to ensure that each selected user does not

have any remaining records in the training set. This

verification ensures a properly simulated cold-start

problem.

4.5 Evaluation Criteria

As mentioned earlier, k-NN is widely employed in CF

systems. Consequently, it is used as our baseline as

well as the base learner in our feature subspace

ensemble. The mean absolute error (MAE) measure,

which indicates the deviation between predicted and

Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems

117

Table 2: Model accuracy for the MovieLense dataset.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Popularity Split

kNN

38.50

38.43

38.28

38.37

38.44

EM-kNN

98.45

98.47

98.44

98.50

98.47

EM-Subspace

98.81

99.18

98.83

98.86

98.68

Random Split

kNN

39.08

38.94

38.91

39.11

39.22

EM-kNN

81.88

81.76

81.81

81.87

81.83

EM-Subspace

86.55

88.51

87.23

87.14

86.38

Table 3: Model accuracy for the Serendipity dataset.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Popularity

Split

kNN

42.18

42.35

43.29

43.07

44.83

EM-kNN

81.84

81.63

81.87

82.58

EM-Subspace

83.14

83.18

84.04

84.17

81.60

Random

Split

kNN

43.87

44.18

45.08

45.24

46.51

EM-kNN

64.43

65.07

65.23

65.33

66.47

EM-Subspace

67.78

68.40

69.27

69.21

69.77

Table 4: MAE results for popularity split test method.

kNN

EM-kNN

EM-Subspace

Serendipity

MovieLense

Serendipity

MovieLense

Serendipity

MovieLense

Iteration 1

0.214

0.237

0.120

0.039

0.167

0.106

Iteration 2

0.213

0.237

0.119

0.039

0.170

0.108

Iteration 3

0.211

0.237

0.119

0.039

0.167

0.110

Iteration 4

0.211

0.237

0.118

0.039

0.167

0.110

Iteration 5

0.210

0.237

0.118

0.039

0.167

0.095

Table 5: MAE results for random split rest method.

kNN

EM-kNN

EM-Subspace

Serendipity

MovieLense

Serendipity

MovieLense

Serendipity

MovieLense

Iteration 1

0.210

0.237

0.175

0.116

0.204

0.164

Iteration 2

0.210

0.237

0.173

0.116

0.201

0.161

Iteration 3

0.209

0.237

0.171

0.116

0.199

0.168

Iteration 4

0.206

0.237

0.170

0.116

0.201

0.166

Iteration 5

0.205

0.236

0.168

0.116

0.198

0.163

Table 6: F-measure results for popularity split method.

kNN

EM-kNN

EM-Subspace

Serendipity

MovieLense

Serendipity

MovieLense

Serendipity

MovieLense

Iteration 1

0.594

0.352

0.818

0.984

0.830

0.988

Iteration 2

0.595

0.351

0.816

0.985

0.831

0.992

Iteration 3

0.604

0.350

0.819

0.984

0.840

0.988

Iteration 4

0.602

0.351

0.826

0.985

0.841

0.989

Iteration 5

0.619

0.352

0.826

0.985

0.815

0.987

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Table 7: F-measure for the random split test method.

kNN

EM-kNN

EM-Subspace

Serendipity

MovieLense

Serendipity

MovieLense

Serendipity

MovieLense

Iteration 1

0.610

0.359

0.629

0.817

0.656

0.864

Iteration 2

0.613

0.358

0.636

0.816

0.660

0.884

Iteration 3

0.622

0.357

0.636

0.816

0.671

0.872

Iteration 4

0.623

0.360

0.637

0.817

0.668

0.871

Iteration 5

0.635

0.361

0.650

0.817

0.673

0.863

Table 8: Test subject from MovieLense dataset.

Popular users

Random split

User ID

#Rating

User ID

#Rating

599

1096

226

474

1280

225

414

1491

282

190

182

805

304

194

477

772

603

773

374

448

698

412

288

724

450

274

780

510

677

602

118

actual rating, is employed as a predictive measure

(Chaaya et al., 2017). In addition, the model accuracy

and the F-measure (geometric mean of recall and

precision) are employed to determine the usefulness

of the recommendation list (Chaaya et al., 2017).

5 RESULTS AND DISCUSSIONS

In this section we discuss the performance of the

model in terms of accuracy, MAE, and F-measure

(Chaaya et al., 2017). Individual users are taken into

account in our evaluation.

5.1 System Evaluation

Table 2 and Table 3 show the classification accuracy

of the PUPP framework system for random and

popularity split. In both cases, active learning

improves the performance—by 39.66% for the

Serendipity data set and 59.95% for the MovieLense

data set. When considering the random split results,

we notice increases of 20.56% for the Serendipity

data set and 42.8% for the MovieLense data set.

These results are obtained using the EM clustering

technique.

In addition, we enhanced the performance of the

traditional CF framework by introducing the

subspace method. Recall that instead of using the k-

NN algorithm as a single classifier, we apply an

ensemble subspace method using k-NN as a base

learner and a subspace of 50% features. Again, we

notice improvement over the traditional CF system.

Specifically, the random split method improves

results by 23.91% for the Serendipity data set and

47.47% for the MovieLense data set compared to the

traditional framework. Also, using the popularity split

method, the accuracy increases by 40.96% and

60.31%, respectively. From Figure 2 and Figure 3,

one may conclude that the popularity split method

always results in a much higher accuracy.

Figure 2: Accuracy for the MovieLense dataset.

Figure 3: Accuracy for the Serendipity dataset.

Table 6 and Table 7 show the results for the F-

measure, which again confirm the benefit of focusing

Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems

119

on popular users while training. The same

observation holds when the MAE metric is employed.

Table 9 shows a summary of the improvement in

percentage over the traditional CF framework for

both data sets. Notice that these improvements were

calculated only for the first iteration, since we are

interested in the immediate, cold-start problem. The

outcome of the last four iterations confirms that the

system can make appropriate recommendations to

new users while performing adequately for existing

users.

Table 9: Improvement in predictive accuracy measures for

system-wide performance over traditional CF.

Framework

Accuracy

Increase by %

F-measure

Increase by %

MAE Decrease

by %

Dataset

Popularity test method

EM-CF

39.99

0.224

0.094

Serendipity

59.95

0.632

0.198

MovieLense

EM-

Subspace-

40.96

0.236

0.047

Serendipity

60.31

0.636

0.131

MovieLense

Random split test method

EM-CF

20.87

0.019

0.035

Serendipity

42.80

0.581

0.243

MovieLense

EM-

Subspace-

23.91

0.046

0.006

Serendipity

47.47

0.628

0.195

MovieLense

5.2 Statistical Validation

This section discusses the results of our statistical

significance testing, using the Friedman test: the

confidence level was set to   . That is, we

wish to determine whether there is any statistical

significance between the performance of the baseline

CF method using k-NN and the two variants of our

PUPP system (EM and EM-Subspace).

In this validation, the Friedman yields a p-value

of  for the Serendipity data set, and a p-

value of  for the MovieLense data set.

Therefore, the null hypothesis is rejected for both data

sets, which means there is a significant difference

among the three frameworks. We report the results of

the pairwise comparisons in Figure 4 and Figure 5.

Furthermore, to determine if there is a significant

difference between each pair, we perform the

Nemenyi post-hoc test. As shown in Table 10 there

is a significant difference among three pairs: EM-kNN

versus kNN, EM-Subspace versus kNN, and kNN

versus EM-kNN. These results confirm that the

system benefits from soft clustering and active

learning. There is no statistical difference between the

versions that use a baseline learning (k-NN) when

compared to an ensemble, which indicates that a

single classifier may be employed against these data

sets. These results confirm our earlier discussion in

which EM-k-NN and EM-subspace, when used with

the popularity split method, have a significantly better

performance when compared with random split.

These two variants also outperform the traditional CF

framework.

5.3 Prediction Rate

To further validate our approach, we considered the

user prediction rate. In this section, the prediction

rates for 10 users from the MovieLense data set are

presented. From Table 11 one may see that EM-kNN

has the best prediction rates of all. However, we

noticed that after the third iteration, when random

split is employed, the prediction rate begins to

decrease, at least for some users. Also, by taking into

consideration the overall performance of the system,

it may be concluded that EM-subspace presents the

best performance against these data sets when

compared to the other two models.

6 CONCLUSION AND FUTURE

WORK

In this paper, we presented the PUPP framework

designed to address the cold-start problem in CF

recommendation systems. Our results show the

benefit of user segmentation based on soft clustering

and the use of active learning to improve predictions

for new users. The results also demonstrate the

advantages of focusing on frequent or popular users

to improve classification accuracy.

In our current approach, we included two

classification algorithms in our experimentation, and

we plan to extend this work to include other

approaches. In our future work, we will also

investigate the suitability of deep learning methods.

Specifically, we are researching the use of deep

composite models for optimal user segmentation and

personalization (Zhang et al., 2019). Furthermore,

this framework was tested in an offline setting; future

plans include testing it in a real-world setting.

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

120

Figure 4: Friedman test mean ranks for the Serendipity dataset.

Figure 5: Friedman test mean ranks for the Serendipity dataset.

Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems

121

Table 10: Nemenyi   s.

Serendipity Dataset

P-kNN

P-EM-kNN

P-EM-Subspace

R-kNN

R-EM-kNN

P-EM-kNN

0.005178

P-EM-Subspace

0.000708

0.995925

R-kNN

0.958997

0.074302

0.016639

R-EM-kNN

0.538193

0.427525

0.168134

0.958997

R-EM-Subspace

0.113891

0.91341

0.65049

0.538193

0.958997

P-kNN

P-EM-kNN

P-EM-Subspace

R-kNN

R-EM-kNN

MovieLense Dataset

P-kNN

P-EM-kNN

P-EM-Subspace

R-kNN

R-EM-kNN

P-EM-kNN

0.009435

P-EM-Subspace

0.000343

0.958997

R-kNN

0.958997

0.113891

0.009435

R-EM-kNN

0.538193

0.113891

0.958997

R-EM-Subspace

0.113891

0.958997

0.538193

0.958997

Table 11: New user Prediction accuracy.

Popular user

UserID

182

274

288

414

448

474

477

599

603

80%

100%

86%

100%

85%

80%

EM-CF

100%

EM-subspace-CF

91%

90%

91%

92%

91%

92%

91%

90%

Random split

UserID

182

274

288

414

448

474

477

599

603

71%

52%

48%

61%

41%

56%

71%

50%

55%

76%

EM-CF

100%

EM-subspace-CF

63%

61%

62%

58%

62%

59%

63%

61%

63%

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