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Book ChapterDOI

A New Information Theory-Based Serendipitous Algorithm Design

09 Jul 2017-Vol. 10274, pp 314-327

TL;DR: By applying the designed algorithm to a game-based application in a real life experiment with target users, it is found that comparing to the conventional designed method; the proposed algorithm has successfully provided more possibilities to the participants to experience serendipitous encountering.

AbstractThe development of information technology has stimulated an increasing number of researchers to investigate how to provide serendipitous experience to users in the digital environment, especially in the fields of information research and recommendation systems. Although a number of achievements have been made in understanding the nature of serendipity in the context of information research, few of these achievements have been employed in the design of information systems. This paper proposes a new serendipitous recommendation algorithm based on previous empirical studies by taking into considerations of the three important elements of serendipity, namely “unexpectedness”, “insight” and “value”. We consider our design of the algorithm as an important attempt to bridge the research fruits between the two areas of information research and recommendation systems. By applying the designed algorithm to a game-based application in a real life experiment with target users, we have found that comparing to the conventional designed method; the proposed algorithm has successfully provided more possibilities to the participants to experience serendipitous encountering.

Topics: Information system (57%), Serendipity (52%), Algorithm design (52%), Empirical research (51%), Recommender system (50%)

Summary (4 min read)

1 Introduction

  • Serendipity is widely experienced in human history, it is defined as “an unexpected experience prompted by an individual’s valuable interaction with ideas, information, objects, or phenomena” [1].
  • So far studies relating to serendipity mainly focus on the following two directions: theoretical studies in the area of information research which aim to investigate the nature of serendipity [2–4], and the empirical studies with the purpose to develop applications or algorithms that provide users with serendipitous encountering especially in the digital environment [5–7].
  • One of the areas which try to employ serendipity applications is the design of recommender system.
  • The overloaded information in the cyber space has made current users no longer satisfied by recommending them those “accurate” information, instead, users aims to be recommended with the information that are more serendipitous and interesting to them [8–10].
  • A rising concern identified in their reviewing of relevant studies is that those discoveries from information research regarding the nature © Springer International Publishing AG 2017 S. Yamamoto (Ed.): HIMI 2017, Part II, LNCS 10274, pp. 314–327, 2017.

2 Problem and Research Question

  • Recommender system researchers often consider serendipity as “unexpected” and “useful” [11], and have designed recommendation algorithms through either content-based filtering [12] or collaborative filtering [13].
  • “Unexpectedness” is considered as the encountered information should be unexpected or a surprise to the information actor, while “value” specifies that the encountered information should be considered as useful and beneficial to the information actor.
  • “Insight” is considered as an ability to find some clue in current environment, then “making connections” between the clue and one’s previous knowledge or experience, and finally shift the attention to the new discovered clue [15].
  • Some researchers have found such ability of “making connections” is actually a key facet in experiencing serendipity [4] and can be quite different among individuals and result in a range of serendipity encounterers from the super-encounterers to occasional-encounterers [16].

3 Proposed Algorithm

  • There are two major concerns in providing serendipitous encountering in the recommendation system design: the first concern is how to balance “unexpectedness” and “useful”.
  • As pointed out by [14], there should be “a most preferred distance” between the two values, as the high level of unexpectedness may cause user’s dissatisfaction of the recommended information, while users may also lose interest to that information with a low unexpectedness.
  • The second concern is how to combine “insight” into system design to stimulate the process of “making connections”.
  • The following part of this section illustrates a detailed implementation of the algorithm.

1. Target user

  • All the categories are arranged through the value of their weights in the user profile.
  • The weight can either be a given weight by the dataset or calculated through clustering analysis [19].
  • For each category set Ci, consider Ci = {a1, a2, a3… ai … an}, where ai is the corresponded attribute to each vector Ci.
  • In particular, for each ai represents the dimension according to which a new user profile may be produced (i.e. author of literatures; musicians).
  • The values for each ai are also arranged by their weight in each vector Ci and can be calculated through semantic analysis such as the tf*idf weight (term-frequency times inverse document frequency) calculation [20]: wðt; dÞ ¼ tft;d log Ndft ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P i tfti;d 2 log Ndfti 2 s ð2Þ.

4. Iteration and End condition

  • The iteration to find the next user would not continue until it meets the following two end conditions: the generated user is no longer new to all the previous generated users; PðUi U1j.
  • Þ comes too large, the recommended information may fail to bring the target user with the sense of unexpectedness, as the recommendation may probably have been acknowledged by the user; however, if the value of PðUi U1j Þ is too small, the recommended information may be too irrelevant to the target user and he/she may lose interest on it.
  • Once the recommendation list is generated within the threshold d, they can be recommended to the target user by selecting the item with the highest values of PðUi U1j Þ.

6. An example of the proposed algorithm

  • The author names of the literatures are set as the attributes for each category and according to the tf*idf weight calculation, there are three values {a1, a2, a3} in category A with the weight W’A = {0.6, 0.3, 0.1}.
  • Set the threshold d as 0.06, then the iteration of the algorithm stops and recommend literatures of category F in d1’s profile to Ann, in addition with the relevant information of d1 and a1.
  • The recommended information can be “these papers (category F) are most stored by d1, who had published papers (d1, d2, d3, d4) with a1 before”.

7. Description of the Proposed Algorithm

  • The proposed algorithm is collaborative filtering based, hence it is more appropriate to those dataset whose content is generated by different users, according to which the next user’s profile will be easier to produce for a current user.
  • The proposed algorithm relates with serendipity from the following three aspects: – Unexpectedness: by setting the value of probability.
  • As aforementioned that the ability to connect the new clue with previous knowledge/experience is a key element in the occurrence of serendipity, and thus there is a necessity for the designers to provide the design clues can contribute to a customer’s noticeability or attention to connect the provided information with his/her personal profile.
  • In the provided example of Fig. 1, such insight is provided by showing the relationship between d1 and the target user, who had published paper together before.
  • By generating the next user’s file according to the weight arrangement of the attributes; those with larger weights are considered as priorities, also known as – Value.

4 Empirical Study

  • According to the information research, studying serendipity in a controlled experiment always has negatively influences on the participants [21, 22]; in addition, serendipity is such a subjective phenomenon that it is tightly closed to the participant’s own experience or knowledge [4, 15].
  • A hint to address the problem may rise from Shute’s [23] stealth assessment theory where the assessments or inferences of conceptions or models that is elusive to humans is embedded into new computer-based technologies such as games.
  • In the centre of Shute’s theory is the Evident-Centred Design (ECD), where a player’s abilities and understandings, especially those that cannot be directly observed by researchers (e.g. critical thinking, problem solving) is reflected through the embedded tasks or situations in the design, such as the interaction processes of the game.
  • Serendipity is exactly such a phenomenon that cannot be observed directly by the researchers; however, during the process of game-playing, participants would naturally produce sequences of actions while performing the designed tasks and hence provides us with possible evidences to access the encounter of serendipity.
  • Based on the above discussion, the authors have then employed the algorithm into a game-based application and have conducted an empirical experiment to investigate whether their proposed algorithm could provide serendipitous encountering to researchers.

4.1 Participant

  • 28 PhD students (14 males and 14 females) from different disciplines are invited to the study.
  • They were asked to conduct a drawing game on a mobile application which was developed by the research group.

4.2 Game-Based Application

  • The developed game is an android-based drawing game, which involves the following stages: Memorising and sketching Each participant was given a picture in the very beginning for observation.
  • Participant was then asked to layout the colour features of the picture based on the memory.
  • A time clock is set during this stage where the maximum observation time is 30 s and the maximum sketch time for each participant is 120 s. Retrieving.
  • When a participant finishes sketching, a group of 30 images is displayed to the participant for retrieving whether or not his/her drawing picture was contained in the provided pictures.
  • Or the participant only needs to click “Next” button.

4.3 Embedded Algorithm and Comparison

  • – Embed proposed algorithm into the developed application.
  • The next step is to embed the proposed algorithm into the application.
  • For each PhD’s supervisor, the co-author from their publications is a large weight attribute in the supervisor’s profile, also known as Assumption 2.
  • As a comparison, each participant was also given the pictures that without the serendipitous information from their proposed algorithm (Fig. 4).
  • Two cover pictures from the “Nature” website (www.nature.com) were selected to the participant, together with the description of the picture on the website (Fig. 5b).

4.4 Evaluation

  • The traditional measurement of serendipity in the area of recommender systems is often based upon the conventional perception on serendipity, where it is considered with the two main characters of “unexpected” and “useful” [11, 14, 24].
  • This is because in some cases, whether or not the recommended information is “useful” or “beneficial” to the participant needs to be further identified, and such identification may probably start from “interesting” or “relevant” [17].
  • Therefore, the evaluation on serendipity in their empirical study is also identified from the four dimensions of “unexpected”, “interesting”, “relevant” and “beneficial”.
  • After a participant finished sketching all the pictures, he/she was given a questionnaire with the four dimensions, and with each dimension a Likert scale from one represents “not at all” to five represents “extremely”.
  • In addition, a 15 min post-interview is carried out right after each participant finished their sketching.

1. Questionnaire

  • In total, 20 effective questionnaires were picked out from the 28 participants, as the other eight participants were too concentrated in the gameplay and failed to read the related information of the picture.
  • These questionnaires were the feedbacks of 40 pictures of the conventional way of “pic-and-info” and the other 40 pictures based on their designed algorithm.
  • According to the four identified dimensions of unexpected, interested, related and beneficial, it is obvious that comparing with the conventional way of “pic-and-info”, their designed algorithm is more possible to result in participant’s serendipitous encountering.

2. Interview

  • During the interviews, most participants reported their senses of serendipity relating to the serendipitous algorithm designed pictures from the following two perspectives: .
  • All the participants reported that they had experienced “unexpectedness” because of the relationship between the picture and the provided information: I’ve never thought the picture is related to my supervisor!.
  • I’m interested about it and will check the details of the paper later.
  • Over eight participants expressed their requirements to add an external link of the presented information (e.g. published paper of …).
  • By contrast, most participants have reported a less interest in the conventional “pic-and-info”, this also reflects the important role of “relatedness” played in the design of the algorithms.

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A New Information Theory-Based
Serendipitous Algorithm Design
Xiaosong Zhou
1
, Zhan Xu
1
, Xu Sun
1(&)
, and Qingfeng Wang
2
1
Faculty of Science and Engineering,
University of Nottingham Ningbo China, Ningbo, China
Xu.sun@nottingham.edu.cn
2
Business School, University of Nottingham Ningbo China, Ningbo, China
Abstract. The development of information technology has stimulated an
increasing number of researchers to investigate how to provide serendipitous
experience to users in the digital environment, especially in the elds of
information research and recommendation systems. Although a number of
achievements have been made in understanding the nature of serendipity in the
context of information research, few of these achievements have been employed
in the design of information systems. This paper proposes a new serendipitous
recommendation algorithm based on previous empirical studies by taking into
considerations of the three important elements of serendipity, namely unex-
pectedness, insight and value. We consider our design of the algorithm as
an important attempt to bridge the research fruits between the two areas of
information research and recommendation systems. By applying the designed
algorithm to a game-based application in a real life experiment with target users,
we have found that comparing to the conventional designed method; the pro-
posed algorithm has successfully provided more possibilities to the participants
to experience serendipitous encountering.
Keywords: Serendipity
Recommendation system Information theory
1 Introduction
Serendipity is widely exp erienced in human history, it is dened as an unexpected
experience prompted by an individuals valuable interaction with ideas, information,
objects, or phenomena [1]. So far studies relating to serendipity mainly focus on the
following two directions: theoretical studies in the area of information research which
aim to investigate the nature of serendipity [24], and the empirical studies with the
purpose to develop applications or algorithms that provide users with serendipitous
encountering especially in the digital environment [57].
One of the areas which try to employ serendipity applications is the design of
recommender system. The overloaded information in the cyber space has made current
users no longer satised by recommending them those accurate information, instead,
users aims to be recommended with the information that are more serendipitous and
interesting to them [810]. However, a rising concern identied in our reviewing of
relevant studies is that those discoveries from information research regarding the nature
© Springer International Publishing AG 2017
S. Yamamoto (Ed.): HIMI 2017, Part II, LNCS 10274, pp. 314327, 2017.
DOI: 10.1007/978-3-319-58524-6_26

of serendipity do not receive sufcient attentions in the recommender system designs.
This paper proposes a new algor ithm to support serendipitous recommendation by
applying recent research fruits on serendipity in the area of information research.
2 Problem and Research Question
Recommender system researchers often consider serendipity as unexpected and
useful [11], and have designed recommendation algorithms through either
content-based ltering [12] or collaborative ltering [13]. However, most of the rec-
ommendation algorithms mainly focus on providing unexpectedness to the users, and
treated the usefulness as only a metric value to measure the effectiveness of their
algorithms rather than considering it as a design clue [14].
As a comparison, serendipity in information research is often considered with three
main characteristics: unexpectedness, insight and value [4]. Unexpectedness is con-
sidered as the encountered information should be unexpected or a surprise to the
information actor, while value species that the encountered information should be
considered as useful and benecial to the information actor. These two understandings of
unexpectedness and value consist with the current view of serendipity in designing
recommender systems [11, 14]; however, the insight aspect tends to be neglected.
Insight is considered as an ability to nd some clue in curren t environment, then
making connections between the clue and one s previous knowledge or experience,
and nally shift the attention to the new discovered clue [15]. Some researchers have
found such ability of making connections is actually a key facet in experiencing
serendipity [4] and can be quite different among individuals and result in a range of
serendipity encounterers from the super-encounterers to occasional-encounterers [16].
The connections can be made between different pieces of information, people and ideas
[3]; therefore, to support or trigger
connection-making in order to bring more pos-
sibilities of experiencing serendipity have always been considered as an imp ortant
design clue for those information researchers [17, 18].
Based on the discussed issues, we then raise our research question: is it possible to
combine the theoretical studies of serendipity in information research, especially the
ignored aspect of insight or making connection, into the recommender system
design?
Followed by our research question, we proposed a collaborative-ltering based
algorithm by considering the theoretical discoveries of serendipity from the area of
information research. Based on the discovery from information research that serendipity
is often encountered in a relaxed and leisure personal state [1, 3], we then applied the
algorithm into a game based application and conducted an empirical experiment.
3 Proposed Algorithm
There are two major concerns in providing serendipitous encountering in the recom-
mendation system design: the rst concern is how to balance unexpectedness and
useful. As pointed out by [14], there should be a most preferred distance between
A New Information Theory-Based Serendipitous Algorithm Design 315

the two values, as the high level of unexpectedness may cause user s dissatisfaction of
the recommended information, while users may also lose interest to that information
with a low unexpectedness. The second concern is how to combine insight into
system design to stimulate the process of making connections.
The two concerns are addressed from the following perspective of relev ance with
two hypotheses:
Hypothesis 1: Given the information that is highly relevant to a users personal
prole, the information would also of a high potential value to the user;
Hypothesis 2: A user will be unexpected to the information that is relevant to his
prole while is not previ ous acknowledged or known by the user.
Consider a target user A, who is the user that will be provided with the recom-
mended information, a user B who is highly relevant to user A and a user C who is
highly relevant to user B while is not known by user A. The user A may experience
serendipity by providing the information of user C, which is unexpected to him/her,
and by providing the relationship between user B and user C, which may further cause
interestingness or usefulness to user A. The following part of this section illustrates a
detailed implementation of the algorithm.
1. Target user
Consider a table of a target user prole U
1
with a category set C = {C
1
,C
2
,C
3
C
i
C
n
}, where C
i
represents the i-th category of the user prole. All the categories are
arranged through the value of their weights in the user prole. The weight can either be
a given weight by the dataset or calculated through clustering analysis [19]. In order to
simplify the introduction of our proposed algorithm here, it is more convenient to set
the weight for each C
i
which is given by the dataset in the very beginning. The weight
of C
i
is larger than C
j
(i > j)inC set:
w
c
¼ w
C
1
; w
C
2
; ...; w
C
i
; ...; w
C
i
; ...; w
C
n
w
C
i
w
C
j
; i [ j

ð1Þ
For each category set C
i
, consider C
i
= {a
1
,a
2
,a
3
a
i
a
n
}, wher e a
i
is the
corresponded attribute to each vector C
i
. In particular, for each a
i
represents the
dimension according to which a new user prole may be produced (i.e. author of
literatures; musicians). The values for each a
i
are also arranged by their weight in each
vector C
i
and can be calculated through semantic analysis such as the tf*idf weight
(term-frequency times inverse document frequency) calculation [20]:
wðt; dÞ¼
tf
t;d
log
N
df
t

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
P
i
tf
t
i
;d

2
log
N
df
t
i

2
s
ð2Þ
Where w(t,d) represents for the weight of a term t in a document d, and it is a
function of the frequency of t in the document (tft,d), the number of documents that
316 X. Zhou et al.

contain the term (dft) and the number of documents in the collection (N). As a result,
the weight for a category set C
i
is determined by the weight of each attribute in the set:
w
c
i
¼ w
a
1
; w
a
2
; ...; w
a
i
; .. .; w
a
j
; ...; w
a
n
w
a
i
w
a
j
; i [ j

ð3Þ
2. Screen the weight
As been pre-dened that C
1
with the largest weight in the C set and a
1
with the largest
weight in the C
i
set. Set a threshold s to eliminate the low weight value from the user
prole U
1
:
w
c
i
¼ w
a
1
; w
a
2
; ...; w
a
i
; .. .; w
a
j
; ...; w
a
n
w
a
i
w
a
j
; i [ j

ð4Þ
Similarly, set a threshold h to eliminate the low weight value from the C
i
set:
w
c
i
¼ w
C
i
a
1
; w
C
i
a
2
; w
C
i
a
3
; ...; w
C
i
a
i
w
C
i
a
i
h
jfg
ð5Þ
3. Generate a new user prole
A new user prole U
i+1
is produce d according to each a
i
in the C
i
set. Here, the
generation of the user prole arrang es from the largest weight of w
C
i
;a
1
to the smallest
weight of w
C
i
;a
i
.
4. Iteration and End condition
Based on the weight arrangement in a user prole, it is intuitional that for an attribute a
i
with a large weight, it is also with more possibility for the current user to have
acknowledged about the information of a
i
. In other words, the probability for a current
user U
i
to make connection with the next user prole U
i+1
is proportional to the weight
of the attribute in current user prole:
PðU
i þ 1
U
i
j
Þ¼kw
c
i
w
c
i
;a
i
ð6Þ
where k is the proportionality coefcient of the probability to the relev ant weight.
The probability of making connections by target user U
1
to i-th user can be further
extended if only the generated user is always new to the prior generated ones:
PðU
i
U
1
j
Þ¼PðU
2
U
1
j
ÞPðU
3
U
2
j
Þ... PðU
i
U
i1
j
Þð7Þ
The iteration to nd the next user would not continue until it meets the following
two end conditions:
the generated user is no longer new to all the previous generated users;
PðU
i
U
1
j
Þ comes to a threshold d , where d represents an appropriate threshold of the
probability.
A New Information Theory-Based Serendipitous Algorithm Design 317

The reason to set the threshold d is to ensure the effectiveness of the iteration
process. This is because if PðU
i
U
1
j
Þ comes too large, the recommended information
may fail to bring the target user with the sense of unexpectedness, as the recommen-
dation may probably have been acknowledged by the user; however, if the value of
PðU
i
U
1
j
Þ is too small, the recommended information may be too irrelevant to the target
user and he/she may lose interest on it. Hence the setting of the threshold d is a very
important step for the iteration process and it needs to be further identied based on
empirical studies in the future. Once the recommendation list is generated within the
threshold d, they can be recommended to the target user by selecting the item with the
highest values of PðU
i
U
1
j
Þ.
5. Recommendation
When the iteration is nished, the content with the largest weighted category in current
candidate will be provided to the target user, in ad dition with the relevant information
of the previous searched users that result in the current user.
6. An example of the proposed algorithm
An example of the proposed algorithm is provided in Fig. 1. Consider Ann as the target
user (U
1
) with different literature categories of {A, B, C} in her person al library, whose
weight is {0.5, 0.3, 0.2} (Fig. 1a). The author names of the literatures are set as the
attributes for each category and according to the tf*idf weight calculation, there are
three values {a
1
,a
2
,a
3
} in category A with the weight WA = {0.6, 0.3, 0.1}. Set k =1
for each probability of the current user to nd the next user pro le, the probability for
Ann to nd a1s prole (U
2
) can be calculated according to Eq. (6):
PðU
2
U
1
j
Þ¼w
A
w
A;a
1
¼ 0: 5 0:6 ¼ 0:3 ð8Þ
The prole of a1 is then produced as Fig. 1b. Likewise, among the four authors in
the D catego ry, author d1 (U
3
) weights largest and then produce d1s prole (Fig. 1c):
PðU
3
U
2
j
Þ¼w
D
w
D;d
1
¼ 0:4 0:5 ¼ 0:2 ð9Þ
According to Eq. (7), the probabili ty for Ann (U
1
) to nd d1s prole (U
3
) is:
PðU
3
U
1
j
Þ¼PðU
2
U
1
j
ÞPðU
3
U
2
j
Þ¼0:3 0:2 ¼ 0:06 ð10Þ
Set the threshold d as 0.06, then the iteration of the algorithm stops and recommend
literatures of category F in d1s prole to Ann, in addition with the relevant information
of d1 and a1. For example, the recommended information can be these papers (category
F) are most stored by d1, who had published papers (d1, d2, d3, d4) with a1 before.
7. Description of the Proposed Algorithm
The proposed algorithm is collaborative ltering based, hence it is more appropriate to
those dataset whose content is generated by different users, according to which the next
users prole will be easier to produce for a current user.
318 X. Zhou et al.

Citations
More filters

Journal ArticleDOI
TL;DR: A novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators, achieving the balance between serendipity and accuracy.
Abstract: Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data.

49 citations


Cites background from "A New Information Theory-Based Sere..."

  • ...According to [29] and [30], it can be briefly described as follows....

    [...]


Journal ArticleDOI
Abstract: Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed.

6 citations


Cites background from "A New Information Theory-Based Sere..."

  • ...Zhou et al.'s (2017) algorithm provides users with information that has a small probability of being discovered but has significant relevance....

    [...]


Journal ArticleDOI
Abstract: A recommender system is employed to accurately recommend items, which are expected to attract the user’s attention. The over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even predictable. Novelty and diversity are two partly useful solutions to these problems. However, novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user’s interests. Hence, it is necessary to consider other criteria, such as unexpectedness and relevance. Serendipity is a criterion for making appealing and useful recommendations. The usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and diversity. The bulk of studies of recommender systems have focused on serendipity in recent years. Thus, a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender systems. Accordingly, this paper focuses on the contextual convergence of serendipity definitions, datasets, serendipitous recommendation methods, and their evaluation techniques. Finally, the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future studies. The results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

4 citations


Journal ArticleDOI
TL;DR: This research presents a novel and scalable approach that combines reinforcement learning with reinforcement learning to provide real-time feedback on the quality of a recommender system's recommendations.
Abstract: Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity, which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

3 citations


Book ChapterDOI
19 Jul 2020
TL;DR: This paper introduces CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance and demonstrates a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipsity, into a practical serendIPitous recommender system.
Abstract: The term “serendipity” has been understood narrowly in the Recommender System Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system With lightweight experiments, we have revealed a few runtime issues and further optimized the same We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering The source codes of CHESTNUT are online at https://githubcom/unnc-ucc/CHESTNUT

2 citations


References
More filters


Book ChapterDOI
01 Jan 2007
TL;DR: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests, which are used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale.
Abstract: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

2,167 citations


Proceedings ArticleDOI
26 Sep 2010
TL;DR: It is argued that the new ways of measuring coverage and serendipity reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.
Abstract: When we evaluate the quality of recommender systems (RS), most approaches only focus on the predictive accuracy of these systems. Recent works suggest that beyond accuracy there is a variety of other metrics that should be considered when evaluating a RS. In this paper we focus on two crucial metrics in RS evaluation: coverage and serendipity. Based on a literature review, we first discuss both measurement methods as well as the trade-off between good coverage and serendipity. We then analyze the role of coverage and serendipity as indicators of recommendation quality, present novel ways of how they can be measured and discuss how to interpret the obtained measurements. Overall, we argue that our new ways of measuring these concepts reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.

515 citations



Journal ArticleDOI
TL;DR: This special section on Intelligent Mobile Knowledge Discovery and Management Systems is to bring together top-quality articles on the art and practice of mobile knowledge discovery and management systems that exhibit a level of intelligence.
Abstract: Advances in wireless communication mobile-information infrastructures such as GPS, WiFi, and mobile phone technologies have enabled us to collect, process, and manage massive amounts of mobile data from diverse information sources. These mobile data are fine-grained, information-rich, and provide unparalleled opportunities for us to understand mobile user behaviours and generate useful knowledge, which in turn allows the delivery of intelligence for real-time decision making in various real-world applications. In this context, knowledge discovery is the process of automatic extraction of interesting and useful knowledge from large amounts of mobile data, whereas knowledge management consists of a range of strategies and practices to identify, create, represent, distribute, and enable the adoption of novel insights and experiences for decision making. There is a critical emerging need to investigate knowledge discovery and management issues in the mobile context. The objective of this special section on Intelligent Mobile Knowledge Discovery and Management Systems is to bring together top-quality articles on the art and practice of mobile knowledge discovery and management systems that exhibit a level of intelligence. We received a total of 12 submissions from which 3 articles have been selected for publication after an extensive peer-review process. The first article, entitled \" Mining Geographic-Temporal-Semantic Patterns in Trajectories for Location Prediction \" by Ying et al., has a focus on location prediction by mining human location traces. A unique perspective of this article is to exploit a user's geographic, temporal, and semantic information simultaneously for estimating the probability of a traveler in visiting a location. The key idea underlying this study is the discovery of user trajectory patterns, which are used to capture frequent movements triggered by the user's geographic, temporal, and semantic intentions. The article \" A Framework of Traveling Companion Discovery on Trajectory Data Streams \" by Tang et al. studies the problem of discovering object groups which travel together (i.e., traveling companions) from trajectory data streams. Since the solution of this problem requires a large computational cost because of expensive spatial operations , the authors propose a smart data structure to facilitate scalable and flexible companion discovery from location traces. The article \" Mondrian Tree: A Fast Index for Spatial Alarm Processing \" authored by M. Doo and L. Liu promotes the efficient process of spatial alarms, which remind us of the arrival of a future spatial event. A key research challenge in scaling spatial alarm processing is how to efficiently …

220 citations