RECOMMENDING DOCUMENTS VIA KNOWLEDGE
FLOW-BASED GROUP RECOMMENDATION
Chin-Hui Lai
1
, Duen-Ren Liu
2
and Ya-Ting Chen
2
1
Department of Information Management, Chung Yuan Christian University, Chung-Li, Tao Yuan County, Taiwan
2
Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan
Keywords: Collaborative filtering, Group recommendation, Document recommendation, Knowledge flow.
Abstract: Recommender systems can mitigate the information overload problem and help workers retrieve knowledge
based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-
related codified knowledge (documents) to perform tasks. A worker’s document referencing behaviour can be
modelled as a knowledge flow (KF) to represent the evolution of his/her information needs over time.
Document recommendation methods can proactively support knowledge workers in the performance of tasks
by recommending appropriate documents to meet their information needs. However, most traditional
recommendation methods do not consider workers’ knowledge flows and the information needs of the
majority of a group of workers with similar knowledge flows. A group’s needs may partially reflect the needs
of an individual worker that cannot be inferred from his/her past referencing behaviour. Thus, we leverage the
group perspective to complement the personal perspective by using a hybrid approach, which combines the
KF-based group recommendation method (KFGR) with the user-based collaborative filtering method (UCF).
The proposed hybrid method achieves a trade-off between the group-based and the personalized method by
integrating the merits of both methods. Our experiment results show that the proposed method can enhance
the quality of recommendations made by traditional methods.
1 INTRODUCTION
Because of the rapid development of information
technologies in recent years, it is now relatively easy
to access knowledge resources. In knowledge-
intensive environments, knowledge workers need to
access task-related codified knowledge (documents)
to perform tasks. However, the huge volumes of
documents that exist in various knowledge domains
often lead to information overload. Thus, there is a
need for document recommendation methods that
support knowledge workers as they perform tasks by
recommending appropriate documents to suit their
information needs, i.e., task needs.
Workers may have various information needs
when executing tasks. Because each worker’s
information needs may change over time, we model
a worker’s document referencing behaviour for a
specific task as a knowledge flow (KF) to represent
the evolution of his/her information needs (Lai and
Liu, 2009). From the personal perspective, a
worker’s KF is derived from his/her past referencing
behaviour to represent his/her personal needs. The
topics and documents included in the KF are related
to the worker’s specific personal needs. From the
group perspective, the information needs of the
majority of the group’s members are more important
than those of individual members. A group’s needs
may partially reflect the needs of an individual
worker that cannot be inferred from his/her past
referencing behaviour. In other words, the group’s
knowledge complements that of the individual
worker.
Recommender systems (Konstan et al., 1997,
Balabanovic and Shoham, 1997) can alleviate the
information overload problem and help workers
identify and retrieve needed documents based on
their preferences or information needs. However, the
referencing behaviour of knowledge workers may
vary over time, but most recommendation methods
do not consider workers’ KFs. Because traditional
recommendation methods focus on personalized
recommendations and have some limitations, several
group-based recommendation methods have been
proposed (Jameson, 2004, McCarthy and Anagnost,
1998, O'Connor et al., 2001). Existing group
341
Lai C., Liu D. and Chen Y..
RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION.
DOI: 10.5220/0003486903410349
In Proceedings of the 6th International Conference on Software and Database Technologies (ICSOFT-2011), pages 341-349
ISBN: 978-989-8425-77-5
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

recommendation schemes satisfy the information
needs of most workers in a group, but they often
neglect individual workers’ preferences and do not
consider recommendations in the context of a KF
environment.
In this work, we propose a hybrid
recommendation method that combines a KF-based
group recommendation (KFGR) method with
traditional collaborative filtering method. The
traditional recommendation method focuses on the
personal perspective rather than the group
perspective; however, the group’s information needs
may be important because they partially reflect an
individual’s needs. In other words, the group’s
knowledge may complement that of the individual
worker. Therefore, we take the group perspective
into consideration to offset the drawback of the
personal perspective. The KFGR method is a novel
recommendation method which takes workers’ KFs
and their personal preferences into account to
recommend documents for a group of workers with
similar KFs. The drawback of the group perspective
is that it may not satisfy the information needs of
some individuals, since it focuses on the needs of the
majority of group members. To resolve the problem,
we combine the KFGR method with traditional
recommendation method, i.e., collaborative filtering,
to enhance the quality of recommendations. The
proposed hybrid method achieves a trade-off
between the group-based and personalized methods
by combining the merits of both methods. The
experiment results show that the proposed method
can improve on the quality of recommendations
provided by traditional recommendation methods.
The remainder of this paper is organized as
follows. Section 2 contains a review of related
works. In Section 3, we describe the KF model and
the proposed hybrid recommendation method. In
Section 4, we detail the experiment results and
discuss their implications. Section 5 contains some
concluding remarks.
2 RELATED WORK
2.1 Knowledge Flow
Knowledge flows among people and processes
facilitate knowledge sharing and reuse. The concept
of knowledge flows has been applied in various
domains, e.g., scientific research, communities of
practice, teamwork environments, industry, and
organizations (Zhuge, 2006). KM enhances the
effectiveness of teamwork by accumulating and
disseminating knowledge among team members to
facilitate peer-to-peer knowledge sharing (Zhuge,
2002). Luo et al. (2008) introduced the concept of
textual knowledge flows based on the management
of knowledge maps. In an organization, knowledge
workers normally have various information needs
over time when performing tasks. Thus, we define a
knowledge flow from the perspective of a worker’s
information needs to represent the evolution of
referencing behaviour and the knowledge
accumulated for a specific task (Lai and Liu, 2009).
Then, the KF-based recommendation methods are
proposed for recommending task-related codified
knowledge.
2.2 Information Retrieval and
Task-based Knowledge Support
A knowledge worker may acquire knowledge from a
large number of documents. Since the documents
can reveal the information needs of the knowledge
worker, we need to filter the documents by using
information retrieval (IR) techniques, which enable
us to access specific items of information (Baeza-
Yates and Ribeiro-Neto, 1999).
Information filtering with a similarity-based
approach is often used to locate knowledge items
relevant to the task-at-hand. The discriminating
terms of a task are usually extracted from a
knowledge item/task to form a task profile, which is
used to model a worker’s information needs. For
example, Holz et al. (2005) proposed a similarity-
based approach to organize desktop documents and
proactively deliver task-specific information; while
Liu et al. (2005) presented a K-Support system to
provide effective task support for a task-based
working environment.
2.3 Recommendation
2.3.1 Collaborative Filtering
Collaborative filtering (CF) is widely used in
recommender systems. CF recommends various
items, such as products, movies, and documents,
based on the preferences of people who have the
same or similar interests to those of the target user.
The approach involves two steps: neighbourhood
formation and prediction. The neighbourhood of a
target user is selected according to his/her similarity
to other users, and is computed by Pearson’s
correlation coefficient or the cosine similarity
measure. Either the k-NN (nearest neighbours)
approach or a threshold-based approach is used to
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342

choose n users that are most similar to the target
user. We use a threshold-based approach in this
paper.
2.3.2 Group-based Recommendation
Group recommender systems are used in various
application domains, such as those that recommend
music, movies, TV programs and tourist attractions.
Generally, such systems can be classified as (1)
those that aggregate individual users’
profiles/preferences to form a group’s
profile/preferences (McCarthy and Anagnost, 1998);
and (2) those that merge individual recommendation
lists into a group recommendation list (O'Connor et
al., 2001, McCarthy and Anagnost, 1998, Kim et al.,
2010). Under the first approach, there is a high
probability of discovering valuable
recommendations that will satisfy the majority of the
group’s members. The second approach gives users
more information when they need to make decisions
and the recommendation results are relatively easy
to explain. However, it is not easy to identify
unexpected items, and it is very time-consuming if
the group is large. Therefore, we follow the first
approach and aggregate workers’ topic domains
based on their knowledge flows to generate profiles
for a group.
3 HYBRID PERSONALIZED AND
GROUP-BASED METHOD
3.1 Overview
In a knowledge intensive environment, a high degree
of knowledge sharing can have a significant effect
on the workers’ efficiency. Each worker
accumulates knowledge when he/she executes a
task, and that knowledge can be shared with and
reused by other team members with similar
information needs. In this paper, we propose a
personalized group-based recommendation method,
i.e. KFGR-UCF, to facilitate knowledge sharing
among a group of workers. The method combines
the KF-based group recommendation method
(KFGR) and user-based collaborative filtering
method (UCF) to enhance the quality of document
recommendation.
The rationale behind the proposed model is that a
group’s information needs may partially reflect an
individual member’s information needs that cannot
be inferred from his/her past document referencing
behaviour. In other words, the group’s knowledge
can be used to satisfy the individual member’s
needs. Thus, the group-based method can
complement the personalized method. However, the
group perspective may neglect the specific
information needs of an individual, because it
focuses on the information needs of the majority of
the group’s members. To resolve this problem, our
hybrid recommendation method combines the merits
of the two approaches to improve the
recommendation quality. The group-based method
recommends documents from the perspective of the
majority’s information needs, while the personalized
methods recommend documents according to the
specific needs of an individual.
The proposed recommendation method is
comprised of three phases: 1) compiling individual
knowledge flows (codified-level KFs and topic-level
KFs); 2) grouping knowledge workers and
generating group profiles; and 3) recommending
documents to workers.
The first phase involves three steps: document
profiling, document clustering, and KF generation.
To accomplish tasks, knowledge workers may need
to access various documents, and those documents
can reflect the workers’ preferences or requirements
in different periods. We align the documents in a
sequence, called a codified-level KF. Each
document in the sequence is represented as an n-
dimensional vector comprised of key terms in the
document and their weights. Next, we cluster the
documents into several topics based on their cosine
similarity scores. To observe the evolution of
information needs, we generate a topic-level KF
(TKF) as a topic sequence by mapping the
documents in the codified-level KF into
corresponding clusters (topics).
In the second phase, we group similar knowledge
workers into groups by using a KF similarity
measure derived from the alignment similarity and
aggregate profile similarity (Lai and Liu, 2009). The
KF similarity score indicates whether the
referencing behaviour of two workers is similar.
After grouping the workers, each group’s important
codified knowledge can be elicited from the topics
accessed by the group members. We compile group
profiles to represent each group’s important
knowledge.
In the last phase, we propose a hybrid of KF-
based group recommendation and user-based CF
(KFGR-UCF), which considers both the group and
personal perspectives, to recommend suitable
documents to knowledge workers. The group-based
approach derives a group-based score (preference)
of a group, k, for a target document based on the
RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION
343

topic-level KFs of the group’s members. Note that
similar documents are grouped into clusters (topics),
so topic-level KFs should provide a larger number of
related documents to satisfy workers’ task needs
than codified-level KFs. Thus, the group-based
approach employs the topic-level KF to predict a
group’s ratings on documents.
3.2 Knowledge Flow Model
A worker’s knowledge flow (KF) represents the
evolution of his/her information needs and
preferences during a task’s execution (Lai and Liu,
2009). Workers’ KFs are identified by analyzing
their knowledge referencing behaviour based on
their historical work logs, which contain information
about previously executed tasks, task-related
documents and the accessed time of documents.
A KF comprises two levels: a codified level and
a topic level. The knowledge in the codified-level
indicates the knowledge flow between documents
based on the access time. In most situations, the
knowledge obtained from one document prompts a
knowledge worker to access the next relevant
document (codified knowledge). Hence, the task-
related documents are sorted in order of the times
they were accessed to obtain a document sequence
as the codified-level KF.
Documents with similar concepts and access
times are grouped together automatically to form a
topic-level abstraction of the task knowledge. Note
that each topic may contain several task-related
documents. The codified-level KF is abstracted to
form a topic-level KF, which represents the
transitions between various topics. Since the task
knowledge in the topic level may flow between
topics, it could prompt the worker(s) to retrieve
knowledge from the next related topic.
3.3 Document Profile Generation
Two profiles, a document profile and a topic profile,
are used to represent a worker’s KF. A document
profile can be represented as an n-dimensional
vector comprised of the key terms in the document
and their respective weights derived by the
normalized tf-idf approach. Based on the term
weights, terms with higher values are selected as
discriminative terms to describe the characteristics
of the document. The document profile d
j
is
comprised of these discriminative terms. Let the
document profile be DP
j
=<dt
1j
:dtw
1j
,dt
2j
:dtw
2j
,
…
,
dt
nj
:dtw
nj
>, where dt
ij
is a term i in d
j
and dtw
ij
is the
degree of importance of the term i to the document
d
j
, which is derived by the normalized tf-idf
approach. The document profiles are used to
measure the similarity of the documents
3.4 Knowledge Flow Mining and
Extraction
When performing a task in a knowledge-intensive
and task-based environment, a worker usually
requires a large amount of task-related knowledge to
accomplish the task. By analyzing a worker’s
referencing behaviour for a specific task, the
corresponding knowledge flow of the task is derived
by a knowledge flow extraction method. For a
specific task, the method derives two kinds of KFs, a
codified-level KF and a topic-level KF, to represent
the worker’s information needs. Each worker has
his/her own codified-level KF, which represents
his/her accumulated knowledge for a specific task at
the codified level.
The topic-level KF, which is derived by
clustering documents with similar content and
access times in the codified-level KF, is represented
by a topic sequence. Based on the order of
documents in each worker’s codified-level KF,
documents with similar content are grouped into
clusters by using a hierarchical agglomerative
clustering method with a time variant (HACT)
algorithm. When clustering a series of time-ordered
documents, i.e., the codified-level KF, the algorithm
considers the documents’ contents as well as the
times the documents were accessed.
We adopt the average linkage hierarchical
clustering method (Jain et al., 1999) to group
documents that have similar profiles and are within
the same time window into clusters by using the
cosine measure to calculate the similarity between
the profiles of two documents. Then, the clustering
result with the best quality is selected to derive the
topic-level KF. Note that a cluster represents a topic
set and has a topic profile (derived from the
document cluster), which describes the features of
the topic.
Topic Profile Generation
Documents in the same cluster contain similar
content and form a topic set. The key features of the
cluster are described by a topic profile derived from
the profiles of documents in the cluster. Let
112 2
:,:,,:
xxxxxnxnx
TPf tt ttw tt ttw tt dtw=< >
be the
profile of a topic (cluster) x, where
ix
tt
is a topic
term and
ix
ttw
is the weight of the topic term.
ICSOFT 2011 - 6th International Conference on Software and Data Technologies
344

3.5 Grouping Knowledge Workers and
Generating Group Profiles
To find a target worker’s neighbours, we compare
his/her topic-level KF with those of other workers to
compute the similarity of their KFs. Such similarity
measurement is used to indicate whether the KF
referencing behaviour of two workers is similar.
Since each KF is a sequence, the sequence alignment
method (Oguducu and Ozsu, 2006), which computes
the cost of aligning two sequences, can be used to
measure the similarity of two KF sequences. Based
on this concept, we use a hybrid similarity measure,
comprised of the KF alignment similarity and the
aggregated profile similarity, to evaluate the
similarity of two workers’ KFs (Lai and Liu, 2009).
3.5.1 Building Group Profiles
The members of a group have similar KFs because
their information needs are similar; and they usually
need to refer to related documents for a specific
topic. Thus, the group-based approach derives the
group-based score (preference) of a group k for a
target document based on the topic-level KFs (TKFs)
of the group’s members. Since similar documents
are grouped into clusters (topics), a larger number of
related documents that may satisfy workers’ task
needs can be recommended by considering topic-
level KFs rather than codified-level KFs. We
identify the important topics that the members
accessed and compute their weights based on each
member’s KF (Eq. (1)). Let GTR
k,x
be group k’s
accumulated rating for topic x, which indicates the
weight of topic x in group k. In addition, let T
u
be the
set of topics in the topic-level KF of user u, and let
U
k
be the set of users in group k.
k
kuUu
GTS T
∈
= ∪
is
the set of topics accessed by members of group k.
,
,
k
ux
uU
kx
k
PTR
GTR
U
∈
=
∑
(1)
where |U
k
| is the number of workers in the group.
PTR
u,x
is the personal rating of worker u for topic x ,
indicating the importance of topic x to worker u. The
rating is derived by Eq. (2) based on u’s topic-level
knowledge flow, assuming that topic y
t
is the topic
accessed by u at time index t.
,
,
,
1
,
,
,
1
(, )
(, )
now
t
t
now t
now
t
now t
t
uy
uy
tt x y
t
ux
t
uy
tt x y
t
TR tw csim TPf TPf
PTR
tw csim TPf TPf
=
=
××
=
×
∑
∑

(2)
where
,
t
uy
TR


is the average rating of worker u
for topic y
t
;
,
t
uy
TR


is derived by averaging the
ratings of worker u for documents belonging to topic
y
t
. TPf
x
/ TPf
y
is the topic profile of topic x / topic y
t
described in Section 3.4; and csim(TPf
x
, TPf
y
) is the
profile similarity between topic x and topic y
t
measured by the cosine formula. In addition,
,
,
t
now
uy
tt
tw
is the time weight of topic y
t
accessed by worker u at
time t. It is defined as
,
,
t
now
uy
tt
now
tSt
tw
tSt
−
=
−
, where St is the
start time of the worker’s KF and t
now
is the time the
worker accessed the most recent topic in his/her KF.
Based on Eq. (1), we can derive the group’s
ratings for topics based on the members’ personal
ratings for those topics. A higher GTR
k,x
score means
that the topic x is more important to group k.
3.6 Recommendation Phase
This phase combines the KF-based group
recommendation method (KFGR) with the
personalized methods to generate recommendation
lists for workers. In the following sub-sections, we
discuss KFGR and the hybrid method, i.e., the
KFGR-UCF method.
3.6.1 The KFGR Method
Some topics may be of interest or important to the
majority of the group’s members. Since documents
related to those topics will probably satisfy the
workers’ information needs, the proposed group-
based approach considers the importance of the
topics accessed by group members. Let
,ki
Gr
be the
group rating based on the document ratings in
knowledge flows of group members, as shown in
Eq. (3).
,
,,
1
,
,
,
1
()
now
now
M
ui
ui tt
u
ki
M
ui
tt
u
rtw
Gr
tw
=
=
×
=
∑
∑
(3)
where r
u,i
is worker u’s rating for document i,
and
,
,
now
ui
tt
tw
is the time weight of document i that
worker u gives it rating at time t. The value of
,ki
Gr
is derived from the personal ratings of group k’s
members for document i. It is a weighted average
group rating of group k for document i derived by
considering its document ratings given by group
members and its time factors in members’
knowledge flows.
RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION
345

Moreover, group members may access and rate
the target documents, so we also take the members’
ratings into account to obtain the predicted rating of
a document in a group. Let GDR
k,i
be the predicted
group rating of group k for a target document i, as
shown in Eq. (4). The value of GDR
k,i
is derived
from linearly combing two parts: group rating based
on the document ratings of group members and
group rating based on the topic-level KF (TKF). The
group rating based on the document ratings of group
members is obtained by the group members’ ratings
for document i. The group rating based on the TKF
is the weighted sum of group k’s ratings on topics by
using the similarity measures of the topics to the
target document as the weights.
,
,,
,
,
(, )
(1 )
(, )
k
k
ki
ki ki
x
ikx
xGTS
ki
xi
xGTS
GDR Aw Gr
csim TPf DPf GTR
Aw
csim TPf DPf
∈
∈
=×+
×
−×
∑
∑
(4)
where GTR
k,x
is the predicted group rating of
group k for topic x measured by Eq. (1); TPf
x
is the
profile (term vector) of topic x; DPf
i
is the profile
(term vector) of document i; GTS
k
is the topic set of
group k; and
,ki
Gr
is the weighted average group
rating of group k for document i derived by
considering the time factor, as shown in Eq. (3).
Aw
k,i
is the activity weighting of group k for
document i, and is defined as Eq. (5).
,
,
,
2
(1 ) ( ,1), 0
0, 0
k,i
ki
k
ki
ki
M
min if M
Gr
Aw
if M
ββ
⎧
×
+− × >
⎪
⎪
=
⎨
⎪
=
⎪
⎩
(5)
where |M
k,i
| is the number of group members that
rated the target document i; |Gr
k
| is the number of
members in group k; and
β is an adjusting weight
determined by the experimental analysis.
The value of Aw
k,i
is in the range of 0 to 1. It will
be high if most of group members rate the document
i, implying that
,ki
Gr
is reliable for representing
group k’s rating on document i. That is, the group
rating based on the document ratings of group
members (i.e.,
,ki
Gr
) will contribute more to the
predicted group rating, i.e., GDR
k,i
. On the contrary,
if a few group members rate the document i, the
value of Aw
k,i
will be small. Thus, the group rating
based on TKFs will contribute more to the predicted
group rating.
Here, we consider the ratings of group members
who have rated the target document and the
predicted group rating for the document. The latter is
derived as the weighted sum of group k’s ratings for
topics in GTS
k
by using the cosine similarity
between the profiles of the target document and
topics as the weights.
3.6.2 The Hybrid KFGR-UCF Method
In this section, we linearly combine the KFGR
method with user-based CF (UCF) to recommend
documents to a target worker. The recommendation
list is generated by combining the predicted ratings
of KFGR and UCF. As mentioned earlier, KFGR
uses the group’s information needs based on the
members’ KFs to make recommendations. It
recommends a group’s preferred documents to a
target worker, and considers the group members’
preferences (i.e. ratings on target documents) as well
as the group’s accumulated ratings on topics.
Meanwhile, the UCF method recommends
documents to a target worker based the ratings of
workers with similar information needs. The
similarity between workers is determined by
calculating Pearson’s correlation coefficient based
on the workers’ ratings for documents. Thus, the
predicted rating of a document is obtained from
neighbours who have similar preferences to the
target worker and whose similarity scores are higher
than a threshold
θ . To improve the performance of
the KFGR and UCF recommendation methods, we
combine them linearly. Based on the hybrid method,
the predicted rating of worker a for document i,
PDR
a,i
, is derived by Eq. (6).
GDR
k,i
is the predicted rating of group k for
document i based on Eq. (4); Psim(R
a
, R
u
) is
Pearson’s correlation coefficient between user a and
user u measured by their rating vectors R
a
and R
u
;
a
r
and
u
r
are the average ratings of worker a and
worker u respectively; r
u,i
is the rating given by
worker u for document i; and α
KFGR-UCF
is a
parameter used to adjust the weight between group-
based prediction and user-based CF prediction.
,,
,
()
()
+
(,)( )
(1 )
(,)
a i KFGR UCF k i
au ui u
u Neighbor a
KFGR UCF a
au
u Neighbor a
PDR GDR
Psim R R r r
r
Psim R R
α
α
−
∈
−
∈
=×
⎛⎞
×−
⎜⎟
−×+
⎜⎟
⎝⎠
∑
∑
(6)
The value of α
KFGR-UCF
is between 0 and 1. It is
derived from conducting experiments by
systematically adjusting its values in an increment of
0.1. When the value of α
KFGR-UCF
is 1, PDR
a,j
is
mainly derived by the KFGR method. That is, the
recommendations are totally dominated by the group
preferences. In contrast, when the value of α
KFGR-UCF
is 0, PDR
a,i
is mainly derived by UCF method. This
ICSOFT 2011 - 6th International Conference on Software and Data Technologies
346

means that the recommendation is dominated by
personal interests. Thus, the optimal value (i.e., the
lowest MAE value) was chosen as the best setting.
Based on the predicted ratings derived by Eq. (6),
documents with high ratings are used to compile a
recommendation list. Then, the top-N documents are
recommended to the target worker.
4 EXPERIMENTS AND
EVALUATIONS
A number of experiments were conducted to
evaluate the proposed hybrid method. We discuss
the experiment setup and the results in Sections 4.1
and 4.2 respectively.
4.1 Experiment Setup
We collected the data for the experiments from a
laboratory in a research institute. The dataset is
comprised of over 600 documents that had been
accessed by about 60 workers. It also includes usage
logs, which provide information about the workers’
access behaviour, i.e., browsing, rating,
downloading, and uploading documents. The log
data is used to analyze the preferences of each user.
In the laboratory environment, each worker has to
complete a research task during a set time period;
thus, he/she needs to access task-related documents
(research papers). We can discover the workers’
knowledge flows from their usage logs. The ratings
given to documents on a scale of 1 to 5 indicate their
relevance and usefulness to the worker’s task. Then,
we divide the data set into two parts: 70% for
training and 30% for testing.
To measure the recommendation quality of the
methods, we use the Mean Absolute Error (MAE)
which is widely used in recommender systems
(Breese et al., 1998, Herlocker et al., 2004). MAE
measures the average absolute deviation of the
predicted rating and the true rating. The lower the
MAE score, the better the accuracy of the
recommendation method. The MAE is derived by
Eq. (7):
ˆ
N
ii
i
P
r
MAE
N
−
=
∑
(7)
where N is the number of documents,
ˆ
i
P
is the
predicted rating of document i, and r
i
is the real
rating of document i given by the user.
4.2 Experiment Results
In the following sub-sections, we will discuss how to
determine the parameters used in the experiments,
and compare the performance of the proposed
method and the traditional methods.
4.2.1 The Analysis of β
In this experiment, we will discuss how to determine
the value of the activity weighting
β (Eq. (5)) for the
KFGR method. The KFGR method described in
Section 3.6.1 is a hybrid method which linearly
combines two parts of group ratings by using an
activity weighting. One part is the group rating
based on the TKFs, while the other part is the group
rating based on the weighted average ratings of
topics, as shown in Eq. (2) and Eq. (3) respectively.
Because group members’ information need may
change over time, these two parts also takes the time
factor into account. To combine these two parts, the
activity weighting is derived from the majority
opinion of group members on documents, i.e.,
Eq.(4), and is used to adjust the relative importance
between these two parts.
Figure 1: The MAE values under different β for KFGR.
For the KFGR, the activity weighting β is a
decimal which ranges from 0 to 1. The other
parameter (1-
β ) is the weight to adjust the activity
weight by considering how many group members
who have accessed the target document. To obtain
the best MAE score, we systematically adjust the
values of β in increments of 0.1 for the KFGR, as
shown in Figure 1.
For the activity weight of the KFGR method, the
lowest MAE occurs when β is 0.1. Thus, we set
β=0.1 for the activity weighting of the KFGR
method to predict document ratings. When β is 0, the
activity weighting is totally derived from such
majority ratio. However, when β is 1, the activity
weight is also equal to 1 too. The predicted rating of
0.8866
0.8856
0.8982
0.878
0.88
0.882
0.884
0.886
0.888
0.89
0.892
0.894
0.896
0.898
0.9
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
MAE
β
KFGR
RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION
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KFGR is totally derived from the group members’
ratings based on TKFs, i.e. Eq. (3).
4.2.2 The Analysis of Time Factor and
Activity Weighting
In this experiment, we compare KFGR, KFGR-NT,
KFGR(AW=1) and KFGR-NT(AW=1) to analyze
the effects of considering the time factor and the
activity weighting in KFGR and KFGR-NT methods
respectively, as shown in Figure 2. Both KFGR and
KFGR(AW=1) methods take the time factor into
account to obtain the group ratings. In the
KFGR(AW=1) method, the activity weighting is set
as 1 for the predicted ratings of documents.
Similarly, both the KFGR-NT and KFGR-
NT(AW=1) methods do not consider the time factor.
Figure 2: Comparison of KFGR and KFGR-NT.
From Figure 2, KFGR, which considers the time
factor, outperforms KFGR-NT. Also, when setting
the activity weighting as 1, the performance of
KFGR (AW=1) is better than the KFGR-NT
(AW=1). The KFGR method is more capable of
satisfying users’ information needs. In addition, the
KFGR outperforms KFGR (AW=1), while KFGR-
NT outperforms KFGR-NT (AW=1). Thus,
considering the activity weighting based on the
majority ratio is effective in improving the
recommendation quality. The KFGR method has the
best performance of recommendation. In the
following experiments, we consider the time factor
in KFGR, and assess the performance of the
proposed hybrid methods.
4.2.3 Evaluation of the Hybrid KFGR-UCF
Method
Here, we evaluate the performance of UCF and the
hybrid KFGR-UCF. We first determine the value of
the parameter α
KFGR-UCF
for the hybrid KFGR-UCF
method. The parameter is used to adjust the relative
importance of KFGR and UCF, whose value ranges
from 0 to 1. When α
KFGR-UCF
is 0, the predicted
rating is derived entirely by the UCF method;
otherwise, when α
KFGR-UCF
is 1, the predicted rating
is derived entirely by the KFGR method.
Figure 3: Comparison of UCF and KFGR-UCF.
To obtain the best MAE, we systematically
adjust the value of α
KFGR-UCF
in increments of 0.1.
The optimal MAE value (0.8499) is generated by
setting α
KFGR-UCF
at 0.6. The importance weight of
KFGR is 0.6, while that of UCF is 0.4. That is, the
KFGR method is relatively more important than the
UCF method in the hybrid of KFGR-UCF. The bar
chart in Figure 3
: compares the performance of UCF
and KFGR-UCF. Since the KFGR-UCF clearly
outperforms UCF, we conclude that the hybrid
KFGR-UCF method improves the recommendation
quality. More specifically, it is capable of predicting
the information needs of individual users from a
group’s perspective.
5 CONCLUSIONS
We have proposed a hybrid KFGR-UCF method
which combines the KF-based group
recommendation method (KFGR) with the user-
based collaborative filtering method (UCF) to
enhance the quality of recommendations. Our
method recommends documents from two
perspectives, i.e., a group perspective and a personal
perspective. From the personal perspective, some
documents are only relevant to a worker’s specific
information needs, i.e., they are not related to the
group’s information needs. A member’s personal
information needs are derived from his/her previous
referencing behaviour. From the group perspective,
there are some documents that most group members
consider relevant. The group’s information needs
may partially reflect an individual member’s
information needs that cannot be inferred from
his/her past referencing behaviour; hence, the
group’s knowledge can complement the individual
0.8856
0.8964
0.8982
0.9067
0.75
0.8
0.85
0.9
0.95
KFGR KFGR-NT KFGR (AW=1) KFGR-NT(AW=1)
MAE
Methods
0.9182
0.8499
0.75
0.8
0.85
0.9
0.95
UCF KFGR-UCF
MAE
Methods
ICSOFT 2011 - 6th International Conference on Software and Data Technologies
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member’s knowledge. In this work, we take the
group perspective into consideration to offset the
drawback of the personal perspective. However, the
group perspective may neglect the information needs
of an individual because it focuses on the needs of
the majority of the group’s members. Since the
group-based method and the personalized method
have distinct advantages, we combined them to
exploit their respective merits. Our experiment
results show that the hybrid method certainly
improve the recommendation quality.
ACKNOWLEDGEMENTS
This research was supported by the National Science
Council of the Taiwan under the grant NSC 96-
2416-H-009-007-MY3 and NSC 99-2410-H-009-
034-MY3.
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