User-to-User Recommendation using the Concept of Movement Patterns:
A Study using a Dating Social Network
Mohammed Al-Zeyadi, Frans Coenen and Alexei Lisitsa
Department of Computer Science, University of Liverpool, Ashton Building, Ashton Street, Liverpool L69 3BX, U.K.
Movement Pattern Mining, Social Networks, Recommender Systems.
Dating Social Networks (DSN) have become a popular platform for people to look for potential romantic
partners. However, the main challenge is the size of the dating network in terms of the number of registered
users, which makes it impossible for users to conduct extensive searches. DSN systems thus make recommen-
dations, typically based on user profiles, preferences and behaviours. The provision of effective User-to-User
recommendation systems have thus become an essential part of successful dating networks. To date the most
commonly used recommendation technique is founded on the concept of collaborative filtering. In this paper
an alternative approach, founded on the concept of Movement Patterns, is presented. A movement pattern
is a three-part pattern that captures the “traffic” (messaging) between vertices (users) in a DSN. The idea is
that these capture the behaviour of users within a DSN while at the same time capturing the associated profile
and preference data. The idea has been built into a User-to-User recommender system, the RecoMP system.
The system has been evaluated, by comparing its operation with a collaborative filtering systems (the RecoCF
system), using a data set from the Chinese DSN comprising 548, 395 vertices. The reported
evaluation demonstrates that very successful results can be produced, a best average F-score value of 0.961.
Dating Social Networks (DSNs) have become an im-
potent platform for people looking for potential part-
ners online. According to a recent survey
, conducted
in the USA, more than 49 million single people (out
of 54 million) have used DSNs such as eHarmony
and Moreover, according to the same
survey, 20% of current committed relationships be-
gan online. In global terms, Badoo
has become the
world’s largest dating network with more than 346
million registered users and about 350 million mes-
sages sent per day. In a large dating network find-
ing potential partners is time consuming, therefore
many DSNs give compatible partner suggestions; in
the same manner as more general recommender sys-
tems, see for example (Resnick and Varian, 1997).
Recommender systems have been found to provide
significant impact with respect to improving user sat-
isfaction in online retail settings (Sohail et al., 2013;
Wang and Wang, 2014). In contrast, developing a
recommender system for a DSN is more challeng-
ing because the recommender system must satisfy the
preferences of pairs of users (Pizzato et al., 2010) as
opposed to single users. In this paper, we propose a
recommendation system based on the concept of fre-
quently occurring Movement Patterns (MPs).
The MP concept was first proposed in (Al-Zeyadi
et al., 2016). An MP is a three part pattern, extracted
from a graph, comprising a descriptions of: a “from
vertex”, a “to vertex” and a connecting edge. The idea
was originally proposed in the context of analysing
“traffic movement” (real or virtual) in networks, such
as freight distribution networks, social networks and
computer networks, where the edges represent traffic.
The idea being to model “traffic movement” within a
network using the idea of frequently occurring MPs
and then to use these models to predict future move-
ments. This paper makes the observation that the MP
concept can equally well be applied in the context
of recommender systems, more specifically recom-
mender systems embedded into DSNs. If we conceive
of a DSN as a collection of vertices, each representing
an individual, the interchange of messages between
vertices can then be considered to represent the traffic
(edges) between vertices. Frequently occurring MPs
can then be extracted and used to generate recommen-
Al-Zeyadi M., Coenen F. and Lisitsa A.
User-to-User Recommendation using the Concept of Movement Patterns: A Study using a Dating Social Network.
DOI: 10.5220/0006494601730180
In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2017), pages 173-180
ISBN: 978-989-758-271-4
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
dations (to existing users and new users).
Given the above, the main contribution of this pa-
per is an analysis of the usage of the MP concept in
the context of recommender systems. More specif-
ically an algorithm, the RecoMP algorithm, is pro-
posed whereby, given a candidate user, a set of “rec-
ommendations” can be made using extracted MPs.
The utility of the mechanism is illustrated in the con-
text of a DSN where the requirement is that the rec-
ommendations most focus on pairs of users (rather
than single users as is the case of more standard
recommender systems). RecoMP was evaluated us-
ing a real-world DSN dataset comprised of 344, 552
males and 203, 843 female users (thus 548,395 ver-
tices in total), and around 3.5 million edges. The
evaluation was conducted by comparing the proposed
MP based RecoMP algorithm with a benchmark algo-
rithm founded on the concept of collaborative filtering
(Schafer et al., 2007). The results demonstrated that
the proposed approach produced much better recom-
mendations than the RecoCF comparator algorithm,
total average precision, recall and FI values of 0.93,
1.00 and 0.96 were recorded, compared to 0.32, 0.74
and 0.39.
In the era of big data the prevalence of social networks
of all kinds has grown dramatically, which in turn has
led to significant user information overload. Coin-
ciding with this growth is a corresponding desire to
analyse (mine) such networks, typically with a view
to some social and/or economic gain. Typical tasks
are the identification of interacting communities (Oh
et al., 2014), and the identification of “influencers”
and “followers” (Li et al., 2014). In the context of the
work presented in this paper the monitoring of traf-
fic in dynamic networks is of relevance (Al-Zeyadi
et al., 2016; Al-Zeyadi et al., 2017). The idea is to
predict the future behaviour of a related network (or
the same network) according to the current behaviour
exemplified in the network being considered. In (Al-
Zeyadi et al., 2016) the concept of Movement Patterns
(MPs) was proposed, as already introduced in the pre-
vious section. In (Al-Zeyadi et al., 2017) the MP con-
cept was used to analyse the databases associated with
the UKs Cattle Tracking System managed by the UKs
Department for Environment, Food and Rural Affairs
(DEFRA). The database records the movement of all
cattle between pairs of locations in GB. These loca-
tions were viewed as vertices in a network, and the
cattle movements as edges between vertex pairs. The
database was used to generate a collection of time
stamped networks where, for each network, the ver-
tices represented cattle holding areas and the edges
occurrences of cattle movement (traffic). The evalua-
tion reported on in (Al-Zeyadi et al., 2017) indicated
that MPs could be effectively used to predict traffic
movement in previously unseen networks.
Information overload is also of concern in online
retail applications where the user is unable to assim-
ilate the wide range of information available con-
cerning products and services. As a consequence
the solution adopted by the online retail industry is
to make recommendations using what are known as
recommendation or recommender systems (Resnick
and Varian, 1997). Broadly, recommendation sys-
tems can be categorised as being either: Item-to-
Item or User-to-User. The main different being that
User-to-User recommendation systems need to make
reciprocal recommendations (Pizzato et al., 2013).
Well known examples of Item-to-Item recommenda-
tion systems are those embedded in Amazon, Netflix
and Spotify; we are all familiar with the “users who
bought X also bought Y” mantra. Well known ex-
amples of User-to-User recommendation systems are
those embedded in Facebook and Linkedin; the “peo-
ple you might know” mantra. Another example ap-
plication domain where User-to-User recommender
systems are used is Dating Social Networks (DSNs).
Dating Networks have become an impotent tool used
by people looking for potential romantic partners on-
line; for example, as already noted above, the Badoo
DSN has over 340 million registered users.
There has been much work directed at User-to-
User recommendation. Of key concern is the quality
of the recommended matches; poor quality matching
will result in people looking elsewhere. In the con-
text of DSNs Matching is typically done using either:
(i) user profiles, (ii) expressed preferences or (iii) user
behaviour. For example in (Kunegis et al., 2012) the
authors propose a way of modelling both the dual-
ity of users similar to each other and preferences to-
wards other users, by using split-complex numbers.
The authors demonstrated firstly that their unified rep-
resentation was capable of modelling both notions of
relations between users in a joint expression and sec-
ondly that their system could be applied in the context
of recommending potential partners. In (Xia et al.,
2016) the authors introduced a recommendation sys-
tem that made use of profiles and references, and pro-
vided a list of recommendations that a user might
be compatible with by computing a reciprocal score
that measured the compatibility between a user and
each potential dating candidate. In (Tu et al., 2014),
the authors proposed a DSN recommendation frame-
work founded on a Latent Dirichlet Allocation (LDA)
model that learns user preferences from observed user
messaging behaviour and user profile features. How-
ever, the majority of User-to-User DSN recommenda-
tion systems are founded on (graph based) Collabora-
tive Filtering (CF) algorithms (Tu et al., 2014; Krzy-
wicki et al., 2014) that focuses on user behaviour. The
intuition is that user behaviour is a much better indi-
cator for recommendations than user profiles or ex-
pressed preferences (Krzywicki et al., 2014). Exam-
ples where CF filtering has been used in the context of
DSNs can be found in (Cai et al., 2010; Kutty et al.,
2014). Given the popularity, and claimed benefits, of
the CF approach this is the approach with which the
proposed MP based RecoMP algorithm is compared.
For the purpose of the evaluation the authors devel-
oped a bespoke CF based DSN recommendation algo-
rithm called RecoCF, this is described in further detail
in Section 5.
The distinguishing feature between the above
DSN recommender systems and the DM based sys-
tem proposed in this paper is the MP concept. To
the best of the authors’ knowledge there has been no
work directed at user-to-user recommendation using
MPs as presented in this paper. There has of course
been plenty of work directed at finding patterns in
data. The earliest examples are the Frequent Pattern
Mining (FPM) algorithms proposed in the early 1990s
(Agrawal et al., 1994). The main objective being to
discover sets of attribute-value pairings that occur fre-
quently which can then be used to formulate what are
known as association rules which in turn have been
used for recommendation purposes, examples can be
found in (Sandvig et al., 2007; Lin et al., 2002). A fre-
quently quoted disadvantage of FPM is the significant
computation time required to generate large numbers
of patterns (many of which may not even be relevant).
The MP Mining (MPM) concept presented in this pa-
per shares some similarities with the concept of FPM.
However, the distinction between movement patterns
and traditional frequent patterns is that movement pat-
terns are more prescriptive, as will become apparent
from the following section. Note also that the move-
ment patterns of interest with respect to this paper are
traffic movement patterns and not the patterns asso-
ciated with the video surveillance of individuals, ani-
mals or road traffic; a domain where the term “move-
ment pattern” is also sometimes used.
An overview of the proposed MP based DSN recom-
mendation systems is presented in this section. The
section commences, Sub-section 3.1, with a review
of the basic operation of DSN systems. A formalism
for the MP concept is then presented in Sub-section
3.2, followed by a formal definition of the problem
domain and a problem statement in Section 3.3.
3.1 DSN Application Framework
The basic operation of DSNs (see Figure 1), regard-
less of the adopted recommendation system used, is
as follows.
1. Joining the Network. When a new user joins
a DSN a new user profile is created using infor-
mation provide by the new user; information such
as: age, gender, location, job, education, income,
smoking, drinking, religion, hobbies, and so on.
2. Browsing. After the creation of the profile the
new user can browse the profiles of existing users
(as can existing users).
3. One Sides Match. While browsing, users may
send messages to other users.
4. Reciprocal Match. On receipt of a massage a
user can return a message (reciprocate). Where
this happens an edge is established in the DSN.
The strength of an edge can be defined in terms of
the quantity and/or duration of the messages. A
degradation factor can also be applied to take into
account the temporal nature of the network.
Given the large number of users, browsing is unlikely
to be successful, hence DSN systems also provide rec-
ommendations. Recommendations can be made when
a new user joins the network and periodically for ex-
isting users. As already noted, the most commonly
adopted techniques for making recommendations are
founded on some form of Collaborative Filtering.
Figure 1: Example Dating Network.
3.2 Movement Pattern Formalism
From the foregoing we are interested in building a rec-
ommender system for a DSN system founded on the
concept of MPs. In the introduction to this paper it
was noted that a MP is a three-part pattern. More for-
mally a MP comprises a tuple of the form:
hF, E, T i (1)
where F, E and T are sets of attribute values. More
specifically the attribute value set F represents a
“From” (sender) vertex, T a “To” (receiver) vertex,
and E an “Edge” connecting the two vertices describ-
ing the nature of the traffic (details of movement) be-
tween them. We refer to a tuple of this type using the
acronym FET. The minimum number of attribute val-
ues in each part (set) must be at least one. The maxi-
mum number of values depends on the size of the at-
tribute sets to which F, E and T subscribe, an MP can
only feature a maximum of one value per subscribed
attribute. The attribute set to which F and T subscribe
is given by A
= {φ
, φ
, . . .}, whilst the attribute set
for E is given by A
= {ε
, ε
, . . .}. Note that F and
T subscribe to the same attribute set because they are
both movement network vertices, and every vertex (at
least potentially) can be a “from” or a “to” vertex in
the context of MPM. Each attribute in A
and A
has a value domain associated with it.
Any given network can also be represented as a
set of tuples of the form hF, E, T i (Equation 1). In
other words a given network can be encapsulated in
the form of a dataset D = {F
, F
, . . .}, where each
D is a FET. An MP is then a FET that occurs
frequently in D, where frequency is defined in terms
of a frequency threshold σ, a percentage value be-
tween 0.0 and 100.0 indicating the proportion of the
number of occurrences of a particular MP with re-
spect to the total number of records (edges) in the
data set, or data set segment, under consideration.
In the context of DSNs the sets F and T represent
DSN user profiles, while the set E represents the na-
ture of the reciprocal messaging between users. A MP
is then a frequently occurring FET that encompasses
a pair of user profiles and reciprocal messaging be-
haviour. Further details concerning MPs and FETs
can be found in found in (Al-Zeyadi et al., 2016) and
(Al-Zeyadi et al., 2017).
3.3 Problem Statement
In the context of the work presented in this paper a
dating network G is defined in terms of a tuple of the
form hV, Ei, where V is a set of vertices representing
the users of the DSN and E is the set of edges rep-
resenting reciprocal communication between users.
Each vertex v
V is defined by a set of attribute val-
ues representing the profile of the user. In the case
of the dataset used for the evaluation purposes, as re-
ported on later in this paper, 25 different attribute val-
ues were used to describe users profiles. Each edge
E is then defined by a another set of attribute val-
ues describing the nature of the communication. For
the evaluation considered later in this paper only two
edge attribute was considered, “communication type”
and “number of messages sent”, the first had two po-
tential values: Reciprocal and Non reciprocal. The
second had a range of values.
In this section the proposed MP based DSN recom-
mendation algorithm is presented, the RecoMP algo-
rithm. Recall that the idea is to use knowledge of ex-
isting frequently occurring MPs in the DSN to make
recommendations. A particular challenge of finding
frequently occurring MPs in DSNs is the size of the
networks to be considered. The exemplar dataset used
for the evaluation reported on later in this paper com-
prised 548,395 vertices and some 3.5 million edges.
In other words we cannot mine and maintain all the
MPs that might feature in the data set. Note that al-
though the number of MPs generated can be reduced
by using a high σ threshold this is undesirable as we
need to use a low σ threshold so as to ensure no sig-
nificant MPs are missed (the most appropriate value
for σ will be considered in Section 6). The solution
is to mine MPs as required with respect to a specific
user and to consequently generate recommendations
with respect to that specific user. Users would be con-
sidered in turn, but recommendations would be made
periodically. It would therefore not be necessary to
consider all DSN users in one processing run. In ad-
dition, by mining MPs on a required basis, the con-
tinuously evolving (dynamic) nature of DSNs can be
taken into account.
The pseudo code for the RecoMP process is pre-
sented in Algorithm 1. The inputs are: (i) a given user
profile u
, (ii) the set of all user profiles U, (iii) the
DSN represented as a dataset D comprised of a set
of FETs (as described above), and (iv) a desired sup-
port threshold σ. Note that for illustrative purposes,
in Algorithm 1, we have assumed a new user, but this
could equally well be an existing user for whom a new
set of recommendations is to be generated. The out-
put is a set R of recommended users (matches). In-
spection of the algorithm indicates that it comprises
two sub-processes: (i) Mining (lines 7 to 21) and (i)
Recommendation (lines 22 to 28).
1 u
= new joined user profile vector
2 U = Collection of all user profile vectors
3 D = Collection of FETs {r1, r2, ...}
describing network G
4 σ = Support threshold
5 R = Set of recommended users
6 Start:
7 Mining Part:
8 M =
9 D
= Pruning D by looping through D and
considering only FET
where F or T similar
to u
10 S hapeSet = the set of possible shapes
, shape
, . . .}
11 forall shape
ShapeSet do
12 forall r
13 if r
matches shape
14 MP
= MP extracted from r
15 if MP
in M then increment support
16 else M = M
, 1
17 end
18 end
19 forall MP
M do
20 if count for MP
< σ then remove MP
from M
21 end
22 Recommendation Part:
23 forall u
U do
24 forall MP
M do
25 if u
and u
* R then
26 R = R
27 end
28 end
Algorithm 1: The RecoMPA Algorithm.
The mining sub-process is where the relevant
MPs are generated. MPs are stored in a set M =
, count
i, hMP
, count
i, . . .}. On start up (line
8) M is set to the empty set
0. The sub-process com-
mences (line 8) by pruning D to create D
D) so that we are left with a set of FETs where either
the From and/or the To part correspond (are similar)
to u
. This benefit of this pruning is that it results in
a significantly reduced search space. Similarity mea-
surement was conducted using the well known Co-
sine similarity metric calculated as shown in Equation
2 where A and B are the set of attribute values of a
newly joined user, and a selected user in the network,
Similarity = cos(Θ) =
Next (line 9) a “shape set” is generated to support MP
extraction. A shape is a MP template (prototype) with
a particular configuration of attributes taking from the
attribute sets A
and A
without considering the as-
sociated attribute values. Once generated shapes can
be populated with attribute values to give candidate
MPs. The idea is to enhance the efficiency of cal-
culating MPs by considering potential MPs in terms
of the attributes they might contain, as oppose to the
individual attribute values they might contain, given
that size of the set of attributes will be less than the
size of the concatenated set of attribute values. The
maximum number of shapes that can exist in D
given by Equation 3, where |A
| and |A
| are the num-
ber of vertex and edge attributes that feature in D
1) × (2
1) × (2
1) (3)
Returning to algorithm 1 the next step is to popu-
late the set of generated shapes (lines 11 to 18). For
each shape shape
in the shape set, and for each FET
(record) r
in D
, if r
matches shape
it is tem-
porarily stored in a variable MP
. Note that a record
matches a shape
if the attributes featured in the
shape also feature in r
. If MP
is already contained in
M we increment the associated count (line 15), other
wise we add MP
to M with a count of 1. Once all
shapes have been processed we loop through M (lines
19 to 20) and remove all MPs whose support count is
less than σ.
When the set of frequently occurring MPs has
been generated the recommender sub-process is com-
menced (line 22). For each MP MP
in M, and each
user profile (vertex) u
in U, if u
is a subset of either
the From or To part of MP
, and has not previously
been recorded in R, u
is appended to R (line 26). In
this manner a set of recommended users is generated.
Note that shape based approach to MP min-
ing described above lends itself to parallelisation.
Each shape can be populated and the various re-
sulting MP instances counted on a separate process-
ing unit without requiring any messaging between
units. Technologies such as Map Reduce (MR) on
a top of Hadoop (Dean and Ghemawat, 2008) or the
well known Massage Passing Interface (MPI) (Gropp
et al., 1999) would be appropriate here as discussed
in (Al-Zeyadi et al., 2017).
To evaluate the proposed RecoMPA algorithm de-
scribed above a benchmark algorithm was required.
As noted in Section 3, the majority of User-to-User
DSN recommendation systems are founded on (graph
based) Collaborative Filtering (CF) approaches (Tu
et al., 2014; Krzywicki et al., 2014). A benchmark
CF based DSN recommendation algorithm was there-
fore developed, the RecoCF algorithm. The general
methodology of Collaborative Filtering, for any sys-
tem, can be described in two steps:
1. Identify users who share the same vector pattern
with the service user (the user whom the predic-
tion is for).
2. Use the preferences of those users founded in step
1 to create a prediction (recommendation) for the
service user.
The same methodology was adopted with respect
to the purpose built CF based DSN recommendation
RecoCF algorithm. The pseudo code for the RecoCF
algorithm is presented in Algorithm 2. As in the case
of RecoMP algorithm, the RecoCF algorithm takes
the same input except there is no need for a σ thresh-
old. The output, as before, is a set of recommended
users R. The algorithm commences (line 6), as in
the case of the RecoMP algorithm, by pruning the
dataset D to give D
. Then for all records (FETs)
in D
the From and To attribute value sets are
extracted (lines 8 and 9), the sets From
and To
. If
is a subset of u
(the new user profile) the
user profile associated with From
is added to R if it
has no already been included. Similarly if To
is a
subset of u
the user profile associated with To
added to R, again provided if has not already been in-
1 u
= new joined user profile vector
2 U = Collection of all user profile vectors
3 D = Collection of FETs {r1, r2, ...}
describing network G
4 R = Set of recommended users
5 Start:
6 D
= Pruning D by looping through D and
considering only FET
where F or T similar
to u
7 forall D
8 From
= return From part from D
9 To
= return To part from D
10 if From
and From
* R then
11 R = R
12 else if To
and To
* R then
13 R = R
14 end
Algorithm 2: The RecoCF Algorithm.
cluded. The result is a set R of recommended users
This section reports on the evaluation conducted with
respect to the proposed RecoMP algorithm. The eval-
uation was conducted using a FET database extracted
from a dataset obtained from the DSN.
The objectives of the evaluation were to compare the
operation of the proposed MP based RecoMP algo-
rithm in comparison with standard Collaborative Fil-
tering (the RecoCF algorithm from Section 5). The
metrics used for the evaluation were: (i) Recall (R),
(ii) Precision and (iii) F-score (F).
Table 1: TCV results using the RecoMP algorithm.
# 1 0.938 1.000 0.978
# 2 0.908 1.000 0.949
# 3 0.917 1.000 0.956
# 4 0.948 1.000 0.972
# 5 0.948 1.000 0.972
# 6 0.948 1.000 0.972
# 7 0.928 1.000 0.961
# 8 0.928 1.000 0.961
# 9 0.952 1.000 0.974
# 10 0.867 1.000 0.917
Avarage 0.928 1.000 0.961
SD 0.02 0.00 0.02
Table 2: TCV results using the RecoCF algorithm.
# 1 0.217 0.764 0.298
# 2 0.369 0.831 0.470
# 3 0.325 0.760 0.416
# 4 0.305 0.722 0.364
# 5 0.305 0.722 0.364
# 6 0.305 0.722 0.364
# 7 0.333 0.756 0.424
# 8 0.354 0.763 0.416
# 9 0.265 0.683 0.361
# 10 0.446 0.717 0.439
Avarage 0.322 0.744 0.392
SD 0.058 0.038 0.048
6.1 Data Sets
For the conducted evaluation reported on in this pa-
per a dataset was obtained from
. Ji- is the most popular DSN in China; in 2011
~100 ~600 ~1200 ~1800 ~2400 ~3100 ~3700 ~4300 ~5100
Number of messages sent bins
Number of users
Figure 2: Male and Female Normal Distribution.
it was reported t have 40.2 million subscribers (users),
and 4.7 million active monthly subscribers. The data
obtained comprised 548, 395 users (344,552 men and
203, 843 women) and details concerning whether a
user had messaged another (no information quantify-
ing the messaging activity was available). Each user
had a profile and a set of preferences associated with
it. Unlike European or US DSNs,, in line
with other Chinese DSNs, is directed at the (hetero-
sexual) marriage market rather than the shorter term
relationship market, and thus user profiles tend to re-
flect this; profiles comprise: age, height, education,
location, occupation, place of work, income, home
ownership, car ownership and so on. Preferences in-
clude things like: age range, height range, education
and location. The data set was processed firstly so
that each user was defined by a set of 25 (profile and
preference) attributes, thus |A
| = 25. It was then
processed again so as to generate a network where
the vertices represented users. Edges where included
wherever two users had messaged each other, in other
words the messaging was reciprocal, thus |A
| = 1
with only a single value. Unfortunately the nature of
the data set was such that we could not extract a more
comprehensive edge attribute set. Converting this net-
work into a FET database resulted in a database com-
prising 3, 311, 076 records. The normal distribution
of the users’ activity, in terms of the number of mes-
sages sent, is presented in Figure 2. From the figure
it can be seen that the majority of users sent 100 mes-
sages over the considered time frame. Given a new
user (or an existing user for whom we wish to make
a recommendation), if we find a frequent MP within
the existing network where either the From or To part
matches the description (profile) of the new user we
recommend the associated existing users to the new
6.2 Performance Effectiveness of
RecoMP with Respect to RecoCF
To determine the effectiveness of the proposed Re-
coMP algorithm, in comparison with RecoCF, two
sets of experiments were conducted. The comparison
was conducted using a variation of Ten Cross Vali-
dation (TCV) whereby the entire FET
database was divided into tenths and the process run
ten times with a different tenth used for testing. More
specifically for each run a random sample of ten users
was extracted from the testing tenth and used for the
evaluation. In this manner the process of TCV could
be conducted without processing all 548,395 vertices
represented in the database. For both sets of experi-
ments a threshold value of σ = 1.0 was used.
The results are given in Tables 1 and 2, Table 1
gives the results using the RecoMP algorithm while
Table 2 gives the results using the RecoCF algorithm.
The tables give the average Precision (P), Recall (R)
and F-score (F) for each tenth, and a total average and
Standard Deviation (SD).
From the above it can clearly be seen that the
recommendations made using the RecoMP algorithm
are better than those generated using Collaborative
Filtering (the RecoCF algorithm). The total aver-
age recall, precision and F-score using RecoMP were
0.928, 1.000 and 0.961; compared to total average
recall, precision and F-score values of 0.322, 0.744
and 0.392 using RecoCF with small SD values were
recorded. It is also interesting to note that the total
average precision using RecoMP, as before, was fre-
quently 1.000; meaning we often make all the correct
recommendations and no incorrect recommendations.
In this paper, the authors have proposed a recommen-
dation system, directed at Dating Social Networks
(DSN), founded on the concept of Movement Pat-
terns (MP), patterns that capture the nature of traf-
fic movement between vertices in networks. The
idea is to extract frequently occurring MPs from a
current network and use these to inform a User-to-
User recommender DSN system. The idea was built
into an algorithm, the RecoMP algorithm, and tested
by comparing the operation of this algorithm with a
Collaborative Filtering approach, RecoCF algorithm.
For the evaluation a large network, extracted from Ji- DSN system, comprising 3,311,076 ver-
tices (users) was used. Excellent results were pro-
duced, a best total average F-score value of 0.961 was
obtained using the RecoMP algorithm compared to a
value of 0.392 using the RecCF algorithm. However,
for general applicability to large DSN, the efficiency
of the approach needs to be improved. A potential
avenue for future work is thus to investigate the po-
tential for using some form of parallel processing, for
example using the well known Massage Pass Inter-
face (MPI) or Hadoop/MapReduce. One of the advan-
tages offered by the “Shape” based approach to min-
ing MPs, as proposed in this paper, is that it lends it-
self to parallelisation, potentially each possible shape
can be processed using a separate processing unit.
The authors would like to thank the China University
of Science and Technology, and the School of Statis-
tics at the Renmin University of China Statistical Cen-
tre, for providing the dataset used for
evaluation purposes in this paper. Also, the first au-
thor would like to thank the Iraqi Ministry of Higher
Education and Scientific Research, and University of
Al-Qadisiyah, for funding this research.
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