Exploring Strategies to Mitigate Cold Start in Recommender Systems: A
Systematic Literature Mapping
Nathalia Locatelli Cezar
1 a
, Isabela Gasparini
1 b
, Daniel Lichtnow
2 c
Gabriel Machado Lunardi
2 d
and Jos
e Palazzo Moreira de Oliveira
3 e
Universidade do Estado de Santa Catarina (UDESC), R. Paulo Malschitzki 200, Joinville, Brazil
Universidade Federal de Santa Maria (UFSM), Av. Roraima 1000, Santa Maria, Brazil
Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
Recommender Systems, Cold Start, New User Cold Start.
Recommender Systems are designed to provide personalized item recommendations to users based on their
preferences and behavioral patterns, aiming to suggest items that align with their interests and profile. In
Recommender Systems, a common issue arises when the user’s profile is not adequately characterized, par-
ticularly at the initial stages of using the system. This issue has persisted in Recommender Systems since its
inception, commonly known as Cold Start. The Cold Start issue, which impacts new users, is called User Cold
Start. Through a systematic literature mapping, this paper identifies strategies to minimize User Cold Start
without reliance on external sources (such as social networks) or user demographic data for initializing the
profile of new users. The systematic literature mapping results present strategies aimed at mitigating the User
Cold Start Problem, serving as a foundational resource for further enhancements or novel proposals beyond
those identified in the review. Thus, the goal of this work is to understand how to create an initial user profile
before any prior interaction and without using external sources in the recommender system.
Recommender systems provide suggestions for items
with a higher probability of interest to a user (Ricci
et al., 2015). An “item” refers to what the system
recommends to users (Jannach et al., 2010), which
can be movies, books, music, tourist spots, products,
scientific papers, etc.
Since the inception of Recommender Systems,
various algorithms/approaches have been devised to
produce recommendations. Traditionally, three ap-
proaches are mainly cited: (a) Content-Based, where
the user receives recommendations for items similar
to those they preferred in the past; (b) Collaborative
Filtering, where the system recommends items to the
user that users evaluated with a similar profile; and
(c) Hybrid, where approaches are combined to recom-
mend items, aiming to reduce disadvantages present
in a single approach (Burke, 2002). Other approaches
can be cited, like the demographic approach where so-
ciodemographic attributes such as age are used (Ricci
et al., 2015).
Despite the advancements in Recommender Sys-
tems, with numerous published works and the ap-
plication of these systems in various domains, there
is still much to explore, especially regarding some
known problems of these systems (Alyari and Ja-
fari Navimipour, 2018). One of the most well-known
issues is the Cold Start problem. Despite the approach
employed, Recommender Systems grapple with the
Cold Start problem (Ricci et al., 2015). The Cold
Start problem can be defined as the inability to create
reliable recommendations due to a lack of data about
a new user or a new item (Monti et al., 2021). Thus,
there are two types of Cold Start: User Cold Start and
Item Cold Start. New User Cold Start is related to the
fact that a user who has interacted little with the sys-
tem and made a few item evaluations will not initially
receive quality recommendations since little is known
about this user. New Item Cold Start refers to the fact
that items evaluated by few users or not evaluated at
all will not be recommended, something that occurs
in some of the approaches used in Recommender Sys-
tems, specially in Collaborative Filtering.
The Cold Start poses serious challenges to the
value of Recommender Systems, as users who do not
Cezar, N., Gasparini, I., Lichtnow, D., Lunardi, G. and Moreira de Oliveira, J.
Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping.
DOI: 10.5220/0012550700003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 965-972
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
receive appropriate recommendations for their profile
may, in some cases, cease using the system (Panda
and Ray, 2022). In (Panda and Ray, 2022), the authors
emphasize that Cold Start problems represent serious
challenges to the commercial value of Recommender
Systems, where New users who do not find the rec-
ommendations useful would stop using the system,
affecting user engagement and sales.
In the work of (Abdullah et al., 2021), the authors
conclude that little research has been carried out in
academia about the Could Start problem. However,
they found that cold start user recommendation has
frequently been researched in the entertainment do-
main, typically using music and movie data.
Thus, this work aims to identify attempts to min-
imize the Cold Start problem described in academic
literature through a systematic literature mapping.
The focus is on the User Cold Start problem. This
choice was made since User Cold Start is the more
crucial characteristic for user engagement and reten-
tion of new users, and it is not a highlighted prob-
lem in a more significant number of approaches used
in Recommender Systems (see Section 2). Also, the
number of these approaches used to create the users’s
profiles using some prior interaction and using exter-
nal sources for the recommendation system is exten-
sive. Consequently, this paper brings a comprehen-
sive analysis to understand how to create this profile
without those approaches.
This paper is structured as follows. Section 2
presents more details about the Cold Start problem,
along with papers describing how Recommender Sys-
tems has obtained data to identify user preferences
and build their profiles. Section 3 presents the system-
atic mapping carried out. Considering the results of
systematic mapping, Section 4 indicates future works.
Section 5 presents the final considerations.
The user cold start problem is linked to the creation
of user profiles. Understanding the process of cre-
ating user profiles requires knowledge about how to
build and represent these profiles. The user profile
representation depends on the chosen approach within
Recommender Systems. It is also important the way
to gather data for building robust user profiles. The
subsequent sections explore these aspects.
2.1 User Profile Representation and
Recommender Systems Approaches
Cold Start presents itself differently for each rec-
ommendation approach employed. In the Content-
Based approach, where the user receives recommen-
dations for items similar to those they liked in the
past, the problem is particularly pronounced for new
users ((New User Cold Start) (Pazzani and Billsus,
2007). In Collaborative Filtering, in addition to User
Cold Start, there is also the presence of New Item
Cold Start, which is related to the fact that items not
evaluated by any user will not be recommended (Lika
et al., 2014)
The issue lies in the Content-Based approach No-
tably, the Content-Based approach is closely linked
to the field of Information Retrieval, where items are
often characterized by textual descriptions (papers,
books, etc.) (Adomavicius and Tuzhilin, 2005).
In the Content-Based approach, the user’s pro-
file is represented by a set of keywords (Adomavi-
cius and Tuzhilin, 2005). In many Recommender
Systems, this set of keywords in the user profile is
compared with items (papers, books, for example) us-
ing a similarity function, like cosine. Furthermore,
in Content-Based Recommender Systems, TF-IDF -
Term Frequency-Inverse Document Frequency is em-
ployed (TF-IDF is frequently used in Information Re-
trieval Systems). The TF-IDF measures the impor-
tance of a term/word to a document in a collection.
The TF-IDF assigns a weight to term i in document
d. Here, t f
represents the number of occurrences of
term i in document d, and id f
, known as the Inverse
Document Frequency of term i, emphasizes the effect
of terms that frequently occur in an item and are sig-
nificant for determining the relevance of that item. In
Recommender Systems, a numerical value is assigned
to each term/word in a user profile to indicate the sig-
nificance of each term in defining the user’s interests
(Manning et al., 2009).
In Collaborative Filtering, the system typically
identifies the utility of an item based on user ratings
and ratings from other users for existing items. In
(Adomavicius and Tuzhilin, 2005), it is mentioned
that this allows the system to handle any type of item
(often, there is no representation/characterization of
items, or it is not used in the recommendation pro-
cess, making it unnecessary). The problem arises in
systems where Collaborative Filtering is used, as an
item to be recommended needs to be evaluated by
users, giving rise to the New Item Problem or Item
Cold Start.
One of the most widely used algorithms in Rec-
ommender Systems employing Collaborative Filter-
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
ing is the Neighborhood-Based approach, with the
most commonly used variation being User-User if the
system has more items than users. This variation as-
sesses which users have similar tastes (evaluated the
same items and provided similar ratings) compared to
a particular user (Ning et al., 2015).
One of the steps in this approach involves calculat-
ing similarity using the Pearson Coefficient and com-
paring a user with others based on the ratings they
gave to items. The Pearson Coefficient measures the
strength of the relationship between two variables.
Considering this, in the Collaborative Filtering ap-
proach, the user’s profile is represented by a set of
item ratings (Resnick et al., 1994).
When the Recommender System uses a Hybrid
Approach (a mix of Content Based and Collaborative
approaches), the way of representing the user profile
will depend on the kind of item to be recommended.
If an item consists of a textual item (e.g., scientific pa-
pers), it is possible to use a set of words and a set of
ratings to represent the user profile (Burke, 2002).
In a Demographic Approach, data about the user,
like age, will be necessary. In this case, a pair
attribute-value will represent the user profile. The
problem is that in many contexts, demographic data
will be irrelevant. This is the case of a Recommender
System that recommends scientific papers, for exam-
ple, (Tintarev and Masthoff, 2015; Adomavicius and
Tuzhilin, 2015). Considering that User Cold Start
is present in more approaches, the emphasis of the
work is on this problem, which involves acquiring
user preferences.
2.2 User Preference Acquisition
Considering the insights from (Knijnenburg and
Willemsen, 2015), four methods can be regarded as
the most commonly used by Recommendation Sys-
tems to acquire these user preferences: item evalu-
ation through scales, assignment of weights to item
attributes, textual reviews of items, and implicit feed-
The method of item evaluation through rating
scales is the most commonly used explicit evaluation
method, where users employ scales (usually a 5-point
Likert scale) to assign ratings to items according to
their preferences (Cena et al., 2010),(Cosley et al.,
2003),(Dooms et al., 2011),(Gena et al., 2011),(Spar-
ling and Sen, 2011). Sometimes, binary evaluations
are made where users are simply asked if they liked
an item or not (Goldberg et al., 2001; Schafer et al.,
2007). On the other hand, assigning weights to item
attributes originates from the field of decision anal-
ysis, where multi-attribute utility is used as a stan-
dard for decision-making (Bettman et al., 1998). In
this case, a degree of importance is assigned to each
attribute of the item to be recommended, requiring
the user to indicate the degree of importance for each
item. There is a strong dependence on the type of
item to be recommended, as different classes of items
present different attributes (e.g., for a computer, the
amount of memory, processor, and price are attributes
that may have distinct weights for different users).
The values of these attributes can be discrete values
arranged on a scale indicating from the best value to
the worst value, e.g., for the cost attribute of an item,
the values could be “very high, high, medium, low,
very low” (Bettman et al., 1998).
Another method of eliciting user preferences is
through a critique/comment made for the item. For
example, this comment can even be processed auto-
matically to determine whether the critique was posi-
tive or negative. Finally, considering that users may
not always be willing to provide evaluations, indi-
cate which attributes are most important to them in an
item, or elaborate on comments, many Recommen-
dation Systems seek to observe user behavior, i.e.,
their actions, which does not require extra effort from
the user (Ricci et al., 2015; Amatriain and Basilico,
2015). Thus, actions such as viewing item details,
purchasing/consuming, and selecting items for pur-
chase, among others, indicate interest in the item.
Furthermore, user demographic data such as age,
gender, and occupation can be considered for profile
formation (Adomavicius and Tuzhilin, 2015) and the
creation of stereotypes that are associated with pref-
erences for specific items. Demographic data signif-
icantly mitigates issues related to User Cold Start.
Still, this data may not always be available, and its
use may not be suitable for recommending certain
items (e.g., recommender systems for scientific pa-
pers). Some recommender systems utilize user data
obtained from social networks (Son, 2016). However,
the challenge lies in the fact that these data are not al-
ways available. It is important to note that regardless
of the approach used, these ways to acquire informa-
tion can be useful. In this sense, even in the Content-
Based approach, ratings given to items can be utilized.
It is possible, for example, to assign greater weight to
terms in the user’s profile that belong to well-rated
The Systematic Literature Mapping aims to identify
approaches to minimize User Cold Start presented in
Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping
the literature, excluding approaches that use exter-
nal sources to the Recommender System (e.g., Social
Networks) and considering only the information ob-
tained during the user’s initial access to the system.
The Systematic Literature Mapping was based on (Pe-
tersen et al., 2008).
The expected results for the review include (a)
Identifying techniques/strategies to minimize the
Cold Start for new users in Recommender Systems;
(b) Identifying the approaches of Recommender Sys-
tems (predominantly Content-Based, Collaborative,
or Hybrid) where these techniques/strategies have
been employed; (c) Identifying the application do-
mains of Recommender Systems where these tech-
niques/strategies have been utilized; and (d) Identify-
ing the types of user profile representations employed
in the identified works.
3.1 Research Questions
Given the objectives, we have formulated a main re-
search question (MRQ) and secondary research ques-
tions (SRQ). The following section presents these
questions: MRQ What techniques have been em-
ployed to reduce the user Cold Start problem? SRQ1:
How is the initial user profile created in these works?
SRQ2: What approaches are used in Recommender
Systems in these works? SRQ3: What are the appli-
cation domains of these works? SRQ4: How has the
evolution of strategies to minimize Cold Start been
over time?
3.2 Search and Selection of Papers
Through an initial search on works related to the re-
search questions, keywords were identified to form
the search query based initially on two terms: (1)
Recommendation Systems and (2) Cold-Start. The
term User Cold-Start was added as the focus was on
new users. Thus, the works should meet the search
query argument ALL(recommender systems or rec-
ommendation systems) AND ALL(user cold start)
AND ABS(cold start), considering the terms “recom-
mender systems” or “recommendation systems” and
the term “user cold start” anywhere in the paper.
The term “cold start” should be in the paper’s
abstract (“ABS”) to identify papers that address the
Cold Start problem and not just mention the term.
In this sense, it was assumed from some prelimi-
nary searches that papers proposing ways to mini-
mize Cold Start mention the expression “Cold Start”
in their abstract.
Thus, the search strings are defined considering
these criteria and the search engine available in each
digital library. The searches were conducted in Jan-
uary 2023 on the following sources: ACM Digital Li-
brary - https://dl.acm.org/; IEEE - https://www.ieee.
org/ and Scopus - https://www.scopus.com/.
Scopus was considered since its database contains
several other scientific bases (Buchinger et al., 2014).
The number of papers returned by each engine is:
ACM returned 90 papers, Scopus returned 374 and
IEEE returned 44. Out of the 508 documents ob-
tained, an analysis of information in the attributes of
title, abstract, and keywords was conducted, applying
inclusion and exclusion criteria.
The Inclusion Criteria (IC) used were: IC1: The
publication is written in the English; and IC2: The
publication has more than four pages.Out of the 508
documents returned by the search, 469 papers met the
inclusion criteria.
Subsequently, Exclusion Criteria (EC) were ap-
plied, initially consisting of four criteria: EC1: The
publication is not available for fully open access
through the portal;
; EC2: The publication does
not describe a technique to minimize User Cold Start;
EC3: The publication is not a primary scientific pa-
per; EC4: The publication is duplicated (i.e., with the
same Digital Object Identifier - DOI).
It was necessary to read the works to apply some
of the exclusion criteria. To expedite the process, this
reading was conducted in three phases: (1) Reading
of the title, abstract, and keywords; (2) Reading of
the introduction and conclusion; and (3) Complete
reading of the paper. If it was impossible to iden-
tify whether the exclusion criterion was met after the
first phase, the second phase was carried out. When
still insufficient, the third phase was executed. Af-
ter completing this process and considering the four
exclusion criteria, 324 papers remained. However, it
was noted that several works created the user profile
through the analysis of the user’s interaction history
(here, there is no reference to the user’s initial inter-
action, that is, the one made on the first access to the
system, but rather interactions recorded after multi-
ple accesses); several works used demographic data;
and several works used external data sources such as
Social Networks from which the data was extracted.
As the objective of this work is to understand how
to create this profile before any prior interaction and
without using external sources to the recommendation
system, that is, at the moment when the user registers,
a fifth exclusion criterion EC5 was defined.
The EC5 defines: the publication uses the user’s
system access history (not only the initial access), ex-
ternal source (e.g., user’s social networks), or user de-
mographic data as a way to initiate the profile of a new
A free access portal promoted by the government.
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Thus, the EC5 was applied. In the ACM database,
54 papers were identified that used user history, 9 pa-
pers used demographic data, and 3 papers used some
external source. In the IEEE database, 22 papers used
history, 6 papers used demographic data, and 5 papers
used some external source. Finally, in the SCOPUS
database, 168 papers used user history, 33 papers used
demographic data, and 5 papers used some external
source. After applying CE5 and removing duplicate
papers, only 19 works were selected for analysis (see
Table 1.
Table 1: Number of papers returned per EC.
Total 83 345 41 469
EC 1 83 249 41 373
EC 2 74 231 39 344
EC 3 73 216 39 326
EC 4 73 212 39 324
EC 5 7 6 6 19
3.3 Selected Papers: Solutions to
Minimize the Cold Start (RQ)
As achieved statistical data, papers are distributed
across the search engines used. The majority of stud-
ies were obtained from ACM, around 36.84%. Fi-
nally, IEEE and Scopus contribute with 31.57% each
to the total result of papers per search engine.
Considering the way to create the initial user pro-
file from the analysis of the works (Creation of the
Initial User Profile (SRQ1)), it is possible to iden-
tify the following strategies: Strategy 1 Requesting
the user to answer questions; Strategy 2 Requesting
the user’s evaluation for a set of items; Strategy 3
Requesting the user to select items; Strategy 4 Re-
questing the user to select/register keywords/tags; and
Strategy 5 Using popular items among older users (or
users considered experts) of the Recommender Sys-
Strategy 1 consists of asking the users some ques-
tion(s) as soon as they enter the system to identify
their preferences. This is used by (Christakopoulou
et al., 2016), (Walunj et al., 2022), (Okada et al.,
2021). Questions include “What genres of movies do
you enjoy?” or “What places do you enjoy to visit?”.
In the case of Strategy 2, the user is asked to
rate some items at their first entry into the sys-
tem. This is used by (Fel
ıcio et al., 2017), (Lazemi
and Ebrahimpour-Komleh, 2017), (He et al., 2020),
(van der Velde et al., 2021)
Strategy 3 is used by (Hristakeva et al., 2017),
(Crawford, 2012), (Fel
ıcio et al., 2016), (Khan et al.,
2021), (Martins et al., 2013), (Fern
andez et al., 2020).
The technique of selecting/registering key-
words/tags (Strategy 4) is used by(Shi et al., 2021),
(Hristakeva et al., 2017).
Finally, using popular items (Strategy 5) involves
items highly rated by active users or called experts.
This strategy is used by (Chao and Guangcai, 2020),
(Lin et al., 2012), (Amatriain et al., 2009), (Zhang
et al., 2020), (Darshna, 2018).
Approaches Used (SRQ2) - In the ACM database,
6 selected papers are Collaborative Filtering, and 1
is Hybrid Approach. In the IEEE database, the three
main recommendation approaches, Collaborative Fil-
tering (CF), Content-Based (CB), and Hybrid (HB),
are found. In the SCOPUS database, the three main
recommendation approaches are also present.
The application Domains (SRQ3) are presented in
the Table 2.
Table 2: Selected papers per application domains.
Paper Recommended
(Shi et al., 2021) News
(Christakopoulou et al.,
ıcio et al., 2017) Movies
(Hristakeva et al., 2017) Documents
(Chao and Guangcai, 2020) Movies
(Lin et al., 2012) News
(Amatriain et al., 2009) Movies
(Crawford, 2012) Movies
(Walunj et al., 2022) Hotels, restaurants
e tourist spots
(Okada et al., 2021) Songs
ıcio et al., 2016) Art paintings and
(Zhang et al., 2020) Movies
(Khan et al., 2021) Food recipes
(Lazemi and Ebrahimpour-
Komleh, 2017)
(Martins et al., 2013) Videos and songs
(He et al., 2020) Movies
(Darshna, 2018) Songs and movies
(van der Velde et al., 2021) Learning objects
andez et al., 2020) Movies
Regarding the Evolution of Solutions to Minimize
Cold Start Over Time (SRQ4), over the years of pub-
lication of the selected papers, the techniques of re-
questing user evaluation for a set of items and ask-
ing the user to select items are present in a signifi-
cant number of years (Fel
ıcio et al., 2017), (Lazemi
and Ebrahimpour-Komleh, 2017), (He et al., 2020),
(van der Velde et al., 2021), (Hristakeva et al., 2017),
Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping
(Crawford, 2012), (Fel
ıcio et al., 2016), (Khan et al.,
2021), (Martins et al., 2013), (Fern
andez et al.,
2020). From 2016 onward, the technique of ask-
ing the user to answer questions or questionnaires
(Christakopoulou et al., 2016), (Walunj et al., 2022),
(Okada et al., 2021) starts to be seen in publications,
and from 2018 onward, the technique of using pop-
ular items becomes present in the papers (Chao and
Guangcai, 2020), (Lin et al., 2012), (Amatriain et al.,
2009), (Zhang et al., 2020), (Darshna, 2018). In 2021,
the technique of asking the user to select or register
keywords/tags were noticed (Shi et al., 2021), (Hris-
takeva et al., 2017). Over the years, there is also
a growing diversity of techniques, with not just one
technique present in all papers published in the same
The systematic literature mapping allowed us to iden-
tify strategies to reduce the User Cold Start problem.
Based on the mapping, it is possible to define fu-
ture works related to the User Cold Start Problem.
Five strategies were identified: Strategy 1, Request-
ing the user to answer questions; Strategy 2, Request-
ing the user’s evaluation for a set of items; Strategy
3, Requesting the user to select items; Strategy 4, Re-
questing the user to select/register keywords/tags; and
Strategy 5, Using items popular among older users
(or users considered experts) of the Recommendation
These strategies do not involve the user’s previous
interaction with the system, i.e., a user history or the
use of external sources to build the profile (e.g., user
data from social networks) so that the Recommender
System can construct an initial user profile.
Over the years of publication of the selected pa-
pers, the techniques of asking the user to evaluate a
set of items and requesting the user to select items
are present in many papers. From 2016 onwards, the
strategy of requesting the user to respond to ques-
tions/questionnaires started to appear in publications,
and from 2018 onwards, the popular item strategy be-
came prevalent in the papers. Over the years, there
has also been a noticeable increase in the diversity of
strategies, with not just one strategy present in all pa-
pers published in the same year (SRQ3).
Some considerations can be made about these
strategies, aiming for future works. Firstly, many of
these strategies still involve evaluations from other
users, as the new user is not always treated as the first
user when using the system. Therefore, strategies that
can be applied to a new user should be considered,
considering that the new user does not require evalua-
tions from other users. There is a research gap regard-
ing works that use these strategies that do not require
any previous user information before the user enters
the system, as well as not needing a user network like
the above strategy of evaluations from other users. It
can also be noted that a limited number of works deal
with the new user problem in Recommender Systems
that use content-based filtering. This fact had been
observed before the mapping through searches in dig-
ital libraries. Few works bring this approach to mini-
mize the Cold Start associated with the identified gap;
the works found with this approach use strategies of
requesting user responses to questions/questionnaires
or selecting items.
Strategy 1 requires the definition of a question-
naire. This is not easy because it requires knowledge
about the domain. Besides, Users may not be willing
to spend time filling out a questionnaire; they might
respond inadequately and even choose not to use the
system. Strategy 5 can be considered suitable only
if the items consist of texts or are described in tex-
tual format. Thus, it is more suitable for the Rec-
ommender System, which uses a Content-based ap-
proach. Besides, defining how the user will give key-
words/tags is necessary. For example: Will the user
write keywords? How many keywords/tags will the
user select? In the last case, how many keywords/tags
will be presented by the user selected, and which key-
words/tags will be shown to the user? A similar issue
is presented in strategy 2 and in strategy 3. How many
items will the user select/evaluate? How many items
will be presented by the user selected, and which
items will be shown to the user?
Show random items, like in (Crawford, 2012), are
not an ideal solution. The problem is that the user
profile can be limited to part of Recommender Sys-
tem items, and the user profile does not reflect all
users’ interests. Thus, in future works, a need arises
to apply item diversity before generating recommen-
dations in the user profile formation. In this sense, to
develop an initial profile that enables recommending
items encompassing all user preferences, one possi-
bility would be to present diversified items to the user,
allowing them to select what they like.
This study aimed to identify, through a System-
atic Literature Mapping, papers addressing the min-
imization of the Cold Start problem without access
to external sources. As a result, 19 papers presenting
strategies for reducing the Cold Start problem were
identified (MRQ).
The strategies are identified by requesting that
the user responds to questions/questionnaires, ask-
ing for the user’s evaluation of a set of items, re-
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
questing the user to select items, keyword/tag selec-
tion/registration, and finally, popular item strategies
(SRQ1). Additionally, it was observed that most stud-
ies focus on the movie domain and domains such as
gastronomy, learning objects, humor entertainment
items, videos, restaurants, papers, news, hotels, mu-
sic, and art items (SRQ3). Hence, it is noteworthy to
highlight that, for future works, certain aspects elu-
cidated in the current study can be considered when
formulating a strategy to mitigate the Cold Start chal-
lenge in Recommender Systems of any domain.
This research is supported by CNPq/MCTI
10/2023 - UNIVERSAL grant n. 402086/2023-
6, CNPq/MCTI/FNDCT 18/2021 grant n.
405973/2021-7 and CAPES - Financing Code 001.
The research by Jos
e Palazzo M. de Oliveira is
partially supported by CNPq grant 306695/2022-7
PQ-SR. The research by Isabela Gasparini is par-
tially supported by CNPq grant 302959/2023-8 and
FAPESC Edital nº 48/2022 TO n°2023TR000245.
Abdullah, N. A., Rasheed, R. A., Nizam, M. H., and Rah-
man, M. M. (2021). Eliciting auxiliary information
for cold start user recommendation: A survey. Ap-
plied Sciences (Switzerland), 11(20).
Adomavicius, G. and Tuzhilin, A. (2005). Toward the
next generation of recommender systems: A sur-
vey of the state-of-the-art and possible extensions.
IEEE transactions on knowledge and data engineer-
ing, 17(6):734–749.
Adomavicius, G. and Tuzhilin, A. (2015). Context-Aware
Recommender Systems, pages 191–226. Springer US,
Boston, MA.
Alyari, F. and Jafari Navimipour, N. (2018). Recommender
systems: A systematic review of the state of the art
literature and suggestions for future research. Kyber-
netes, 47(5):985–1017.
Amatriain, X. and Basilico, J. D. (2015). Recommender
systems in industry: A netflix case study. In Recom-
mender Systems Handbook.
Amatriain, X., Lathia, N., Pujol, J. M., Kwak, H., and
Oliver, N. (2009). The wisdom of the few: A col-
laborative filtering approach based on expert opin-
ions from the web. Proceedings - 32nd Annual In-
ternational ACM SIGIR Conference on Research and
Development in Information Retrieval, SIGIR 2009,
pages 532–539.
Bettman, J. R., Luce, M. F., and Payne, J. W. (1998).
Constructive Consumer Choice Processes. Journal of
Consumer Research, 25(3):187–217.
Buchinger, D., Cavalcanti, G. A. d. S., and Hounsell,
M. D. S. (2014). Mecanismos de busca acad
uma an
alise quantitativa. Revista Brasileira de
ao Aplicada, 6(1):108–120.
Burke, R. (2002). Hybrid Recommender Systems : Survey
and Experiments. User Modeling and User-Adapted
Interaction, 12(6):331–370.
Cena, F., Vernero, F., and Gena, C. (2010). Towards
a Customization of Rating Scales in Adaptive Sys-
tems. In De Bra, P., Kobsa, A., and Chin, D., edi-
tors, User Modeling, Adaptation, and Personalization,
pages 369–374, Berlin, Heidelberg. Springer Berlin
Chao, X. and Guangcai, C. (2020). Collaborative Filtering
and Leaders’ Advice Based Recommendation System
for Cold Start Users. In Proceedings of the 2020 6th
International Conference on Computing and Artificial
Intelligence, ICCAI ’20, pages 158–164, New York,
NY, USA. Association for Computing Machinery.
Christakopoulou, K., Radlinski, F., and Hofmann, K.
(2016). Towards conversational recommender sys-
tems. Proceedings of the ACM SIGKDD International
Conference on Knowledge Discovery and Data Min-
ing, 13-17-August-2016(3):815–824.
Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., and Riedl,
J. (2003). Is seeing believing? how recommender sys-
tem interfaces affect users’ opinions. In Proceedings
of the SIGCHI Conference on Human Factors in Com-
puting Systems, CHI ’03, page 585–592, New York,
NY, USA. Association for Computing Machinery.
Crawford, D. (2012). Initial Profile Generation in Recom-
mender Systems Using Pairwise Comparison. Com-
munications of the ACM, 36(11):18.
Darshna, P. (2018). Music recommendation based on
content and collaborative approach & reducing cold
start problem. Proceedings of the 2nd International
Conference on Inventive Systems and Control, ICISC
2018, (Icisc):1033–1037.
Dooms, S., De Pessemier, T., and Martens, L. (2011). An
online evaluation of explicit feedback mechanisms for
recommender systems. pages 391–394.
ıcio, C. Z., Paix
ao, K. V., Barcelos, C. A., and Preux,
P. (2017). A multi-Armed bandit model selection for
cold-start user recommendation. UMAP 2017 - Pro-
ceedings of the 25th Conference on User Modeling,
Adaptation and Personalization, pages 32–40.
ıcio, C. Z., De Almeida, C. M., Alves, G., Pereira, F. S.,
ao, K. V., De Amo, S., and Barcelos, C. A. (2016).
Vp-rec: A hybrid image recommender using visual
perception network. pages 70–77.
andez, D., Formoso, V., Cacheda, F., and Carneiro, V.
(2020). A Content-Based Approach to Profile Expan-
sion. International Journal of Uncertainty, Fuzziness
and Knowlege-Based Systems, 28(6):981–1002.
Gena, C., Brogi, R., Cena, F., and Vernero, F. (2011). The
Impact of Rating Scales on User’s Rating Behavior. In
Konstan, J. A., Conejo, R., Marzo, J. L., and Oliver,
N., editors, User Modeling, Adaption and Personal-
ization, pages 123–134, Berlin, Heidelberg. Springer
Berlin Heidelberg.
Exploring Strategies to Mitigate Cold Start in Recommender Systems: A Systematic Literature Mapping
Goldberg, K., Roeder, T., Gupta, D., and Perkins, C. (2001).
Eigentaste: A Constant Time Collaborative Filtering
Algorithm. Information Retrieval, 4(2):133–151.
He, D. P., He, Z. L., and Liu, C. (2020). Recommendation
algorithm combining tag data and naive bayes classifi-
cation. Proceedings - 2020 3rd International Confer-
ence on Electron Device and Mechanical Engineer-
ing, ICEDME 2020, pages 662–666.
Hristakeva, M., Knoth, P., Kershaw, D., Pettit, B., Jack,
K., Rossetti, M., and Vargas, S. (2017). Build-
ing Recommender systems for scholarly information.
ACM International Conference Proceeding Series,
Part F127853:25–32.
Jannach, D., Zanker, M., Felfernig, A., and Friedrich, G.
(2010). Recommender Systems: An Introduction.
Cambridge University Press.
Khan, M. A., Smyth, B., and Coyle, D. (2021). Address-
ing the complexity of personalized, context-aware and
health-aware food recommendations: an ensemble
topic modelling based approach. Journal of Intelli-
gent Information Systems, 57(2):229–269.
Knijnenburg, B. and Willemsen, M. (2015). Evaluating
Recommender Systems with User Experiments, pages
Lazemi, S. and Ebrahimpour-Komleh, H. (2017). Improv-
ing collaborative recommender systems via emotional
features. Application of Information and Communica-
tion Technologies, AICT 2016 - Conference Proceed-
ings, pages 1–5.
Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S.
(2014). Facing the cold start problem in recom-
mender systems. Expert systems with applications,
Lin, C., Xie, R., Li, L., Huang, Z., and Li, T. (2012).
PRemiSE: Personalized News Recommendation via
Implicit Social Experts. In Proceedings of the 21st
ACM International Conference on Information and
Knowledge Management, CIKM ’12, pages 1607–
1611, New York, NY, USA. Association for Comput-
ing Machinery.
Manning, C. D., Raghavan, P., and Sch
utze, H. (2009). An
Introduction to Information RetrievalAn Introduction
to Information Retrieval. Number c. Cambridge Uni-
versity Press.
Martins, E. F., Bel
em, F. M., Almeida, J. M., and
Gonc¸alves, M. (2013). Measuring and addressing the
impact of cold start on associative tag recommenders.
WebMedia 2013 - Proceedings of the 19th Brazilian
Symposium on Multimedia and the Web, pages 325–
Monti, D., Rizzo, G., and Morisio, M. (2021). A systematic
literature review of multicriteria recommender sys-
tems, volume 54. Springer Netherlands.
Ning, X., Desrosiers, C., and Karypis, G. (2015). A
Comprehensive Survey of Neighborhood-Based Rec-
ommendation Methods, pages 37–76. Springer US,
Boston, MA.
Okada, K., Tan, P. X., and Kamioka, E. (2021). Five-Factor
Musical Preference Prediction for Solving New User
Cold-Start Problem in Content-Based Music Recom-
mender System. IISA 2021 - 12th International Con-
ference on Information, Intelligence, Systems and Ap-
Panda, D. K. and Ray, S. (2022). Approaches and algo-
rithms to mitigate cold start problems in recommender
systems: a systematic literature review. Journal of In-
telligent Information Systems, pages 341–366.
Pazzani, M. J. and Billsus, D. (2007). Content-based rec-
ommendation systems. In The adaptive web: methods
and strategies of web personalization, pages 325–341.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M.
(2008). Systematic mapping studies in software engi-
neering. 12th International Conference on Evaluation
and Assessment in Software Engineering, EASE 2008,
(February 2015).
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and
Riedl, J. (1994). Grouplens: An open architecture for
collaborative filtering of netnews. In Proceedings of
the 1994 ACM conference on Computer supported co-
operative work, pages 175–186.
Ricci, F., Rokach, L., and Shapira, B. (2015). Recom-
mender systems: introduction and challenges. Rec-
ommender systems handbook, pages 1–34.
Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S.
(2007). Collaborative Filtering Recommender Sys-
tems, page 291–324. Springer-Verlag, Berlin, Heidel-
Shi, S., Ma, W., Wang, Z., Zhang, M., Fang, K., Xu, J.,
Liu, Y., and Ma, S. (2021). WG4Rec: Modeling Tex-
tual Content with Word Graph for News Recommen-
dation. In Proceedings of the 30th ACM International
Conference on Information & Knowledge Manage-
ment, CIKM ’21, pages 1651–1660, New York, NY,
USA. Association for Computing Machinery.
Son, L. H. (2016). Dealing with the new user cold-start
problem in recommender systems: A comparative re-
view. Information Systems, 58:87–104.
Sparling, E. I. and Sen, S. (2011). Rating: How difficult is
it? In Proceedings of the Fifth ACM Conference on
Recommender Systems, RecSys ’11, page 149–156,
New York, NY, USA. Association for Computing Ma-
Tintarev, N. and Masthoff, J. (2015). Explaining recom-
mendations: Design and evaluation. In Recommender
systems handbook, pages 353–382. Springer.
van der Velde, M., Sense, F., Borst, J., and van Rijn, H.
(2021). Alleviating the Cold Start Problem in Adap-
tive Learning using Data-Driven Difficulty Estimates.
Computational Brain and Behavior, 4(2):231–249.
Walunj, V., Sharma, S., Wagh, A., Solanki, U., and Ma-
hajan, J. (2022). Smart tour advisor using machine
learning and natural language processing. pages 53–
Zhang, Z., Dong, M., Ota, K., and Kudo, Y. (2020). Al-
leviating New User Cold-Start in User-Based Collab-
orative Filtering via Bipartite Network. IEEE Trans-
actions on Computational Social Systems, 7(3):672–
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