Challenges of Serendipity in Recommender Systems
Denis Kotkov, Jari Veijalainen and Shuaiqiang Wang
University of Jyvaskyla, Dept. of Computer Science and Information Systems, P.O.Box 35, FI-40014, Jyvaskyla, Finland
Relevance, Unexpectedness, Novelty, Serendipity, Recommender Systems, Evaluation Metrics, Challenges.
Most recommender systems suggest items similar to a user profile, which results in boring recommendations
limited by user preferences indicated in the system. To overcome this problem, recommender systems should
suggest serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous
to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes
an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned
challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the
paper is to guide and inspire future efforts on serendipity in recommender systems.
With the growth of information on the Internet it be-
comes difficult to find content interesting to a user.
Hopefully, recommender systems are designed to
solve this problem. In this paper, the term recom-
mender system refers to a software tool that suggests
items of use to users (Ricci et al., 2011). An item is a
piece of information that refers to a tangible or digital
object, such as a good, a service or a process that a
recommender system suggests to the user in an inter-
action through the Web, email or text message (Ricci
et al., 2011). For example, an item can be a reference
to a movie, a song or even a friend in an online social
Recommender systems and search engines are dif-
ferent kinds of systems that aim at satisfying user in-
formation needs. Traditionally, a search engine re-
ceives a query and, in some cases, a user profile as an
input and provides a set of the most suitable items in
response (Smyth et al., 2011). In contrast, a recom-
mender system does not receive any query, but a user
profile and returns a set of items users would enjoy
(Ricci et al., 2011). The term user profile refers to ac-
tions a user performed with items in the past. A user
profile is often represented by ratings a user gave to
Recommender systems are widely adopted by
different services to increase turnover (Ricci et al.,
2011). Meanwhile, users need a recommender sys-
tem to discover novel and interesting items, as it is
demanding to search items manually among the over-
whelming number of them (Shani and Gunawardana,
2011; Celma Herrada, 2009).
Most recommendation algorithms are evaluated
based on accuracy that indicates how good an algo-
rithm is at offering interesting items regardless of how
obvious and familiar to a user the suggestions are
(de Gemmis et al., 2015).To achieve high accuracy,
recommender systems tend to suggest items similar
to a user profile (Tacchini, 2012). As a result, the
user receives recommendations only of items similar
to items the user rated initially. Accuracy-based al-
gorithms limit the number of items that can be rec-
ommended to the user (so-called overspecialization
problem), which lowers user satisfaction (Celma Her-
rada, 2009; Tacchini, 2012). To overcome over-
specialization problem and broaden user preferences,
a recommender system should suggest serendipitous
Suggesting serendipitous items is challenging
(Foster and Ford, 2003). Currently, there is no con-
sensus on definition of serendipity in recommender
systems (Maksai et al., 2015; Iaquinta et al., 2010).
It is difficult to investigate serendipity, as the con-
cept includes an emotional dimension (Foster and
Ford, 2003) and serendipitous encounters are very
rare (Andr
e et al., 2009). As different definitions of
serendipity have been proposed (Maksai et al., 2015;
Iaquinta et al., 2010), it is not clear how to mea-
sure serendipity in recommender systems (Murakami
et al., 2008; Zhang et al., 2012).
In this paper we are going to discuss mentioned
challenges to guide and inspire future efforts on
Kotkov, D., Veijalainen, J. and Wang, S.
Challenges of Serendipity in Recommender Systems.
In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 2, pages 251-256
ISBN: 978-989-758-186-1
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
serendipity in recommender systems. We review def-
initions of serendipity. We also review and classify
evaluation metrics to measure serendipity and indi-
cate their advantages and disadvantages.
Suggesting serendipitous items involves certain chal-
lenges. We are going to present the most important of
them. Designing a serendipity-oriented recommenda-
tion algorithm requires to choose suitable objectives.
It is therefore necessary to investigate how to assess
serendipity in recommender systems, which requires
a definition of the concept.
2.1 Definition
It is challenging to define what serendipity is in rec-
ommender systems, what kind of items are serendipi-
tous and why, since serendipity is a complex concept
(Maksai et al., 2015; Iaquinta et al., 2010).
According to the dictionary
, serendipity is “the
faculty of making fortunate discoveries by accident”.
The term was coined by Horace Walpole in the let-
ter to Sir Horace Mann in 1754. The author de-
scribed his unexpected discovery by referencing the
fairy tale, “The Three Princes of Serendip”. Horace
Walpole in his letter explained that the princes were
“always making discoveries, by accidents and sagac-
ity, of things which they were not in quest of (Remer,
One of the examples of serendipity is the discov-
ery of penicillin. On September 3, 1928, Alexander
Fleming was sorting petri dishes and noticed a dish
with a blob of mold. The mold in the dish killed one
type of bacteria, but did not affect another. Later, the
active substance from the mold was named penicillin
and used to treat a wide range of diseases, such as
pneumonia, skin infections or rheumatic fever. The
discovery of penicillin can be regarded as serendipi-
tous, as it led to the result positive for the researcher
and happened by accident.
To introduce discoveries similar to the discovery
of penicillin in recommender systems, it is neces-
sary to define and strictly formalize the concept of
serendipity. We therefore reviewed definitions em-
ployed in publications on recommender systems.
Corneli et al. investigated serendipity in a compu-
tational context including recommender systems and
proposed the framework to describe the concept (Cor-
neli et al., 2014). The authors considered an essential
key condition, focus shift. A focus shift happens when
something that initially was uninteresting, neutral or
even negative becomes interesting.
One of definitions used in recommender systems
was employed by Zhang et al.: “Serendipity repre-
sents the “unusualness” or “surprise” of recommen-
dations” (Zhang et al., 2012). The definition does not
require serendipitous items to be interesting to a user,
but surprising.
In contrast, Maksai et al. indicated that serendip-
itous items must be not only unexpected (surprising),
but also useful to a user: “Serendipity is the quality
of being both unexpected and useful” (Maksai et al.,
Adamopoulos and Tuzhilin used another defini-
tion. The authors mentioned the following compo-
nents related to serendipity: unexpectedness, novelty
and a positive emotional response, which can be re-
garded as relevance of an item for a user:
Serendipity, the most closely related concept
to unexpectedness, involves a positive emo-
tional response of the user about a previ-
ously unknown (novel) [...] serendipitous rec-
ommendations are by definition also novel.
(Adamopoulos and Tuzhilin, 2014).
A similar definition was employed by Iaquinta et
al. According to (Iaquinta et al., 2010), serendipitous
items are interesting, unexpected and novel to a user:
A serendipitous recommendation helps the
user to find a surprisingly interesting item that
she might not have otherwise discovered (or it
would have been really hard to discover). [...]
Serendipity cannot happen if the user already
knows what is recommended to her, because a
serendipitous happening is by definition some-
thing new. Thus the lower is the probabil-
ity that user knows an item, the higher is the
probability that a specific item could result
in a serendipitous recommendation (Iaquinta
et al., 2010).
Definitions used in (Iaquinta et al., 2010) and
(Adamopoulos and Tuzhilin, 2014) seem to corre-
spond to the dictionary definition and the framework
proposed by Corneli et al. As a serendipitous item
is novel and unexpected, the item can be perceived
as uninteresting, at first sight, but eventually the item
will be regarded as interesting, which creates a focus
shift, a necessary condition for serendipity (Corneli
et al., 2014). The definition also corresponds to the
dictionary definition, as novel, unexpected and inter-
esting to a user item is likely to be a “fortunate dis-
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
Publications dedicated to serendipity in recom-
mender systems do not often elaborate the compo-
nents of serendipity (Iaquinta et al., 2010; Maksai
et al., 2015; Zhang et al., 2012). It is not entirely clear
in what sense items should be novel and unexpected
to a user.
Kapoor et al. indicated three different definitions
of novelty in recommender systems (Kapoor et al.,
1. Novel to a recommender system item. A recently
added item that users have not yet assessed.
2. Forgotten item. A user might forget that she con-
sumed the item some time ago in the past.
3. Unknown item. A user has never seen or heard
about the item in her life.
In addition, Shani and Gunawardana suggested that
we may regard a novel item as one not rated by the
target user regardless of whether she is familiar with
the item (Shani and Gunawardana, 2011).
Unexpectedness might also have different mean-
ings depending on expectations of a user. A user
might expect a recommender system to suggest items
similar to her profile, popular among other users or
both similar and popular (Kaminskas and Bridge,
2014; Zheng et al., 2015).
2.1.1 Serendipity in a Context
Most recommender systems do not consider any con-
textual information, such as time, location or mood
of a user (Adomavicius and Tuzhilin, 2011). Mean-
while, the context may significantly affect the rele-
vance of items for a user (Adomavicius and Tuzhilin,
2011). An item that was relevant for a user yester-
day might not be relevant tomorrow. A context may
include any information related to recommendations.
For example, a recommender system may consider
current weather to suggest a place to visit. Context-
aware recommender systems use contextual informa-
tion to suggest items interesting to a user.
Serendipity depends on a context, as each of its
components is context-dependant. An item that was
relevant, novel and unexpected to a user in one con-
text might not be perceived the same in another con-
text. The inclusion of a context affects the definition
of serendipity. For example, contextual unexpected-
ness would indicate how unexpected an item is in a
given context, which might be different from unex-
pectedness in general. Serendipity might consist of
novelty, unexpectedness and relevance in a given con-
As the context has a very broad definition (Dey,
2001), it is challenging to estimate what contextual
information is the most important in a particular situ-
ation. For example, weather is an important factor for
most outdoor activities, while user mood is important
for music suggestion (Kaminskas and Ricci, 2012).
2.1.2 Serendipity in Cross-domain
Recommender Systems
Most recommender systems suggest items from a
single domain, where the term domain refers to “a
set of items that share certain characteristics that
are exploited by a particular recommender system”
ıas et al., 2012). These characteristics
are items’ attributes and users’ ratings. Different do-
mains can be represented by movies and books, songs
and places, MovieLens
movies and Netflix
(Cantador and Cremonesi, 2014).
Recommender systems that suggest items using
multiple domains are called cross-domain recom-
mender systems. Cross-domain recommender sys-
tems can use information from several domains, sug-
gest items from different domains or both consider
different domains and suggest items from them (Can-
tador and Cremonesi, 2014). For example, a cross-
domain recommender system may take into account
movie preferences of a user and places that the user
visits to recommend watching a particular movie in a
cinema suitable for the user.
Consideration of additional domains affects the
definition of serendipity, as cross-domain recom-
mender systems may suggest combinations of items.
It is questionable whether in this case items in the
recommended combination must be novel and unex-
2.1.3 Discussion
According to literature review, to date, there is no con-
sensus on definition of serendipity in recommender
systems (Maksai et al., 2015; Iaquinta et al., 2010).
We suggest that the definition of serendipity should
include combinations of items from different domains
and a context, which might encourage researchers to
propose serendipity-oriented recommendation algo-
rithm that would be more satisfying to users. For ex-
ample, suppose, two young travelers walk in a cold
rain in a foreign city without much money. A rec-
ommendation of a hostel would be obvious in this
situation, as the travelers would look for a hostel on
their own. A suggestion of sleeping in a local cinema,
which would cost less than a hostel, is likely to be
serendipitous in that situation. The recommendation
Challenges of Serendipity in Recommender Systems
Table 1: Notation
I = (i
) the set of items
F = ( f
, f
,..., f
) feature set
i = ( f
, f
,..., f
) representation of item i
U = (u
,..., u
) the set of users
the set of items rated
by user u (user profile)
the set of items
recommended to user u
1 if item i relevant for
user u and 0 otherwise
would be even more satisfying to the travelers if the
recommender system also suggested the longest and
cheapest movie in that cinema and an energetic song
to cheer up the travelers.
2.2 Emotional Dimension
Relevance of an item for a user might depend on user
mood (Kaminskas and Ricci, 2012). This contextual
information is difficult to capture without explicitly
asking the user. As serendipity is a complex con-
cept, which includes relevance (Iaquinta et al., 2010;
Adamopoulos and Tuzhilin, 2014), this concept de-
pends on the current user mood in a higher degree.
An emotional dimension makes serendipity unstable
and therefore difficult to investigate (Foster and Ford,
2.3 Lack of Serendipitous Encounters
As serendipitous items must be relevant, novel and
unexpected to a user, they are rare (Andr
e et al., 2009)
and valuable. Due to the lack of observations it is dif-
ficult to make assumptions regarding serendipity that
would be reasonable in most cases.
2.4 Evaluation Metrics
We are going to review evaluation metrics that mea-
sure serendipity in recommender systems. As dif-
ferent metrics have been proposed (Murakami et al.,
2008; Kaminskas and Bridge, 2014; Zhang et al.,
2012), the section provides their comparison, includ-
ing advantages and disadvantages. To review evalua-
tion metrics, we first present notation in table 1.
The following evaluation metrics consider a rec-
ommender system with I available items and U users.
User u rates or interacts with items I
I. A
recommender system suggests R
items to user u.
Each item i,i I is represented as a vector i =
( f
, f
,..., f
) in a multidimensional feature space
F. For example, a feature can be a genre of a movie
on a web-site. If F = (drama,crime,action) then the
movie The Shawshank Redemption can be repre-
sented as i
= (0.4,0.4,0.1).
Seeking to measure serendipity of a recommender
system, researchers proposed different evaluation
metrics. Based on reviewed literature we classify
them into three categories: content-based unexpect-
edness, collaborative unexpectedness and primitive
recommender-based serendipity.
2.4.1 Content-based Unexpectedness
Content-based unexpectedness metrics are based on
attributes of items. These metrics indicate the dissim-
ilarity of suggestions to a user profile.
One of the content-based unexpectedness metrics
was proposed by Vargas and Castells (Vargas and
Castells, 2011). Later, the metric was adopted by
Kaminskas and Bridge to measure unexpectedness
(Kaminskas and Bridge, 2014). The authors sug-
gested that serendipity consists of two components:
relevance and unexpectedness. Content-based unex-
pectedness metrics can be used to measure unexpect-
edness, while accuracy metrics such as root mean
square error (RMSE), mean absolute error (MAE) or
precision (Ekstrand et al., 2011) can be used to assess
relevance. The metric is calculated as follows:
(i,u) =
1 sim(i, j) (1)
where sim(i, j) is any kind of similarity between items
i and j. For example, it might be content-based cosine
distance (Lops et al., 2011).
2.4.2 Collaborative Unexpectedness
Collaborative unexpectedness metrics are based on
ratings users gave to items. Kaminskas and Bridge
proposed a metric that can measure unexpectedness
based on user ratings (Kaminskas and Bridge, 2014).
User ratings can indicate similarities between items.
Items can be considered similar if they are rated by the
same set of users. The authors therefore proposed a
co-occurrence unexpectedness metric, which is based
on normalized point-wise mutual information:
(i,u) =
p(i, j)
p(i)p( j)
p(i, j)
where p(i) is the probability that users have rated item
i, while p(i, j) is the probability that the same users
have rated items i and j.
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
2.4.3 Primitive Recommender-based Serendipity
The metric is based on suggestions generated by a
primitive recommender system, which is expected
to generate recommendations with low unexpected-
ness. Originally the metric was proposed by Mu-
rakami (Murakami et al., 2008) and later modified
(Ge et al., 2010; Adamopoulos and Tuzhilin, 2014).
Modification proposed by Adamopoulos and Tuzhilin
is calculated as follows:
(u) =
(i), (3)
where PM is a set of items generated by a primi-
tive recommender system, while E
is a set items
that matches interests of user u. In the experiments
conducted by Adamopoulos and Tuzhilin, the primi-
tive recommender system generated non-personalized
recommendations consisting of popular and highly
rated items. Meanwhile, E
contained items similar
to what user u consumes.
2.4.4 Analysis of the Evaluation Metrics
Content-based and collaborative metrics capture the
difference between recommended items and a user
profile, but have a disadvantage. These metrics mea-
sure unexpectedness separately from relevance. The
high score of both metrics can be obtained by suggest-
ing many unexpected irrelevant and expected relevant
items that would probably not be serendipitous.
Depending on a primitive recommender system,
the metrics based on a primitive recommender sys-
tem capture item popularity and dissimilarity to a
user profile, but also have a disadvantage. Primitive
recommender-based metrics are sensitive to a prim-
itive recommender system (Kaminskas and Bridge,
2014). By changing this parameter, one might obtain
contradictory results.
Designing a serendipity-oriented algorithm that
takes into account a context and combinations of
items from different domains requires a correspond-
ing serendipity definition and serendipity metric. An
item might be represented by a combination of items
from different domains and considered serendipitous,
depending on a particular situation. The reviewed
metrics disregard a context and additional domains
due to the lack of serendipity definitions that consider
this information. One of the reasons might be that
recommender systems do not usually have the infor-
mation on the context. Another reason might be the
disadvantages of offline evaluation.
Even offline evaluation of only relevance with-
out considering the context or additional domains
may not correspond to results of experiments in-
volving real users (Said et al., 2013; Garcin et al.,
2014). Offline evaluation may help choose candidate
algorithms (Shani and Gunawardana, 2011), but on-
line evaluation is still necessary, especially in assess-
ing serendipity, as serendipitous items are novel by
definition (Iaquinta et al., 2010; Adamopoulos and
Tuzhilin, 2014) and it is difficult to assess whether
a user is familiar with an item without asking her.
In this paper, we discussed challenges of serendipity
in recommender systems. Serendipity is challenging
to investigate, as it includes an emotional dimension,
which is difficult to capture, and serendipitous en-
counters are very rare, since serendipity is a complex
concept that includes other concepts.
According to the reviewed literature, currently
there is no consensus on definition of serendipity
in recommender systems, which makes it difficult
to measure the concept. The reviewed serendip-
ity evaluation metrics can be divided into three
categories: content-based unexpectedness, collabo-
rative unexpectedness and primitive recommender-
based serendipity. The main disadvantage of content-
based and collaborative unexpectedness metrics is
that they measure unexpectedness separately from rel-
evance, which might cause mistakes. The main disad-
vantage of primitive recommender-based serendipity
metrics is that they are sensitive to a primitive recom-
In our future work, we are going to propose a def-
inition of serendipity in recommender systems, de-
velop serendipity metrics and design recommenda-
tion algorithms that suggest serendipitous items. We
are also planning to conduct experiments using pre-
collected datasets and involving real users. We hope
that this paper will guide and inspire future research
on recommendation algorithms focused on user satis-
The research at the University of Jyv
a was per-
formed in the MineSocMed project, partially sup-
ported by the Academy of Finland, grant #268078.
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