Long Term Goal Oriented Recommender Systems
Amir Hossein Nabizadeh
, Al
ıpio M
ario Jorge
and Jos
e Paulo Leal
LIAAD, INESC TEC, Porto, Portugal
CRACS, INESC TEC, Porto, Portugal
Faculdade de Ci
encias, Universidade do Porto, Porto, Portugal
Recommender System, Collaborative Filtering, Content Based Filtering, Persuasive Recommender System,
Learning Design, Matrix Factorization, Long Term Recommender System.
The main goal of recommender systems is to assist users in finding items of their interest in very large collec-
tions. The use of good automatic recommendation promotes customer loyalty and user satisfaction because it
helps users to attain their goals. Current methods focus on the immediate value of recommendations and are
evaluated as such. This is insufficient for long term goals, either defined by users or by platform managers.
This is of interest in recommending learning resources to learn a target concept, and also when a company is
organizing a campaign to lead users to buy certain products or moving to a different customer segment. There-
fore, we believe that it would be useful to develop recommendation algorithms that promote the goals of users
and platform managers (e.g. e-shop manager, e-learning tutor, ministry of culture promotor). Accordingly, we
must define appropriate evaluation methodologies and demonstrate the concept on practical cases.
Current recommender systems focus on the immedi-
ate value of recommendations. This is insufficient for
achieving long term goals. For that, we need Long
Term Recommender Systems (LTRS) that are able
to guide the users to predefined areas in item space
and/or to achieve other types of goals. In such a sce-
nario, user guidance would be achieved by generat-
ing a sequence of relevant recommendations through
time. For example, in the case of music, companies
may utilize these systems in order to guide the users
from a preferred music genre to a target genre. There-
fore, LTRS can be used to gradually influence users’
interests through time. Companies can use LTRS to
improve their profit on selected products (e.g. new
products or products of a new segment). Another ex-
ample for the potential application of LTRS is in e-
learning. In this case, learning objects can be recom-
mended to students with a higher level objective in
view. By applying this system in the scope of learn-
ing, the activities can be more productive and less
time consuming for both the student and teacher.
In a more abstract way, the main question we pro-
pose is: how can we generate recommendation se-
quences that successfully conduct the user to a goal
(e.g. a target area of the item space), while satisfy-
ing user requirements? A goal can be defined as a
pre-determined area in the item space of interest to
both user and platform manager. To attain a long term
goal, a recommendation algorithm must act strategi-
cally and not merely tactically.
The quality of a LTRS can be measured on how
it can influence users’ decisions and guide the users
towards a predefined target area. Although there are
several techniques to evaluate the accuracy of RS such
as Precision, Recall or MSE, these are not enough
to assess the strategic capabilities of a recommenda-
tion system. Therefore, we argue that complmentary
means of evaluation will be needed for LTRS.
One important feature of LTRS is the ability to
persuade users. Persuasive systems have been pro-
posed by Fogg (Fogg, 2002). The aim is to use com-
puters to positively influence how users think and
act. Persuasive technology emphasizes on the social
role of computers. Since then, a few studies applied
this technology for recommender systems (Yoo et al.,
2012), which mainly focused on psychological as-
pects. We believe that the use of persuasiveness prin-
ciples can improve the effectiveness of recommenda-
tions in order to guide the users towards the long term
LTRS must also be able to handle issues such as
Sparsity and Scalability (Burke, 2002). Matrix Fac-
Hossein Nabizadeh A., Mário Jorge A. and Paulo Leal J..
Long Term Goal Oriented Recommender Systems.
DOI: 10.5220/0005493505520557
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 552-557
ISBN: 978-989-758-106-9
2015 SCITEPRESS (Science and Technology Publications, Lda.)
torization (MF) algorithms have been successful in
dealing with both problems (Gillis, 2012). Therefore,
we believe that MF approaches can be use as a core
of LTRS.
Finally, we plan to adopt Learning Design (LD)
principles in order to generate recommendation se-
quences. In the e-learning field, the main goal of
LD is to generate a suitable learning path (a sequence
of objects) for users. Several researchers applied
these principles for recommenders (Learning Design
Recommender Systems (LDRS)). LDRS have sev-
eral advantages such as: finding suitable learning ob-
jects, propose a well-defined order of objects (a path)
and recommending not only based on the similar-
ity among objects. Current LDRS are focused on
e-learning applications and require an explicit target
item (competency) (Durand et al., 2013). Different
evaluation measures are used to evaluate the results
of LDRS.
In this position paper, we propose the idea of Long
Term Recommender Systems that guide users toward
a predefined goal by generating relevant recommen-
dations. LTRS will be supported by persuasive tech-
nology, LDRS principles and MF algorithms. In ad-
dition, we plan to design a general evaluation frame-
work in order to assess the results of LTRS.
The remainder of this paper is structured as fol-
lows. Section 2 describes the related work of this
study. The research methodology is detailed in Sec-
tion 3 and then we conclude.
The main goal of recommender systems is to assist
users in finding items of their interest in large col-
lections. Items can be movies, news, articles, music,
places to visit, etc. The creation of the World Wide
Web and its later shift to the Web 2.0 in the 90s, made
users face the issue of data overload (Kantor et al.,
2011). Since then, the quantity of data and informa-
tion has been dramatically increasing daily.
Due to the data overload issue, finding the in-
terests of customers efficiently is a critical problem.
One of the tools which help users in filtering informa-
tion are search engines. To personalize the informa-
tion filtering process, the community created recom-
mender systems (RS). The main functionality of such
a system is to sort and filter the items according to
an acquired profile of each user (Resnick and Varian,
There are many techniques and algorithms that
can be applied by recommendation systems to gen-
erate the recommendations. These techniques can be
classified in 3 main categories:
Content based recommendation (CBF): which
recommends new items to users that are compara-
ble in content with items that a user has purchased
already (Balabanovi
c and Shoham, 1997).
Collaborative Filtering (CF): recommends items
based on other users that have similar interests
or other items that have the same characteristics
c and Shoham, 1997).
Hybrid method: is a combination of CBF and CF.
(Melville and Sindhwani, 2010).
2.1 Persuasive Recommendation System
LTRS are aimed at generating recommendations that
satisfy users’ requirements and persuade users to fol-
low them. However, if the users do not follow
them, the main goal cannot be accomplished. Persua-
sive technology as proposed by Fogg in 2002 (Fogg,
2002), applies computers to influence users’ thoughts
and actions. In RS field, this technology focuses on
psychological aspect of recommendations and clari-
fies how recommendations can be represented that in-
fluence users more.
Persuasive RS are based on two theories: Media
equation theory (Reeves and Nass, 1997) and Com-
munication persuasion paradigm (O’Keefe, 2002).
According to communication persuasion paradigm,
the scope that a person influence others depends on
(1) form and content, (2) source, (3) the receiver char-
acteristics, (4) contextual factor (O’Keefe, 2002). If
we see the system (in our case a RS) as a person that
we communicate with (media equation theory), sys-
tem can be seen as a source, user as a receiver and
recommendations as messages. The whole process of
recommending is about a specific context. Recom-
mendations persuade receivers whether to continue
using the system or not (Yoo et al., 2012)
Figure 1: Conceptual framework of persuasive RS (Yoo
et al., 2012).
2.1.1 Source Factors
Source is a system that generates recommendations.
According to (Xiao and Benbasat, 2007), the factors
of source that have effect on persuasiveness of RS are:
type of recommender (CBF, CF, Hybrid); inputs such
as user preferences elicitation methods; ease of gen-
erating the recommendations and giving more control
to users during their interaction with system; process
features (like how system generates the recommen-
dations or providing information about response time
of system; embodied agent features: recommender
systems usually include virtual character conducting
the users through the process. It can be assumed that
recommendations are more convicing if system is per-
sonified. Some of these features are anthropomor-
phism, agent demographics, style of speech and hu-
mor that influence the persuasiveness of recommen-
2.1.2 Message Factors
According to the previous research (Cosley et al.,
2003; Sinha and Swearingen, 2001) which are con-
ducted on the same field content (such as discrep-
ancy, specificity, sidedness) and format of messages
(text, video, audio) are as important as the recommen-
dation system (source) and have significant influence
on users’ evaluation and behavior.
2.1.3 Receiver Factors
User or receiver features that influence persuasiveness
of recommendations such as familiarity, involvement
and knowledge, are detailed as follow:
1. Knowledge: when users do not have sufficient in-
formation about items prefer to use a website or
system which is equipped with a recommendation
system (Doong and Wang, 2011; Perera, 2000).
2. Involvement: when users explicitly participate in
the preference elicitation process, a system can
generate more accurate recommendations (Zanker
et al., 2006; Drenner et al., 2008).
3. Familiarity: users with former experience with
recommenders can trust the better and results
of RS can be more convincing (Swearingen and
Sinha, 2002).
4. Demographic Cues: genders are different in
acceptance of recommendations. For instance,
women assess the quality of recommendations
to a greater extent than men (Doong and Wang,
2011). The users’ culture can also be considered
as a demographic cue (Chen and Pu, 2008). For
example, when a recommendation system sug-
gests alcoholic drinks to a person who has restric-
tions due to his culture, it causes the user to give
a lower rate to the recommendations.
2.2 Learning Design Recommendation
Systems (LDRS)
In the area of e-learning, Learning Design (LD) is an
activity to build an effective learning path (a set of
connected learning objects) by finding suitable learn-
ing objects (Durand et al., 2013). A learning object
is any reusable digital resource which supports the
learning process (Wiley, 2003). Several approaches
applied LD in order to recommend an efficient learn-
ing path. In other words, proposing and recommend-
ing an appropriate order of learning objects can be
considered as the main functionality of LDRS (Car-
chiolo et al., 2010). The main advantages of these
systems are the ability to find adequate learning ob-
jects (items), build an efficient path and avoid to gen-
erate recommendations only based on the similarity
among objects.
Different methods and techniques have been ap-
plied to propose learning design recommenders. For
example, Ullrich and Melis applied Hierarchical Task
Network (HTN) to build an adaptive structured course
generation framework for different goals (Ullrich and
Melis, 2009). Sicilia et al. also used HTN to de-
sign learning scenarios in educational systems (Sicilia
et al., 2006).
Vassileva and Deters utilized decision rules in
a tool that generates individual courses. Their
tool exploits on previous knowledge of a user and
user’s goals. This tool can be updated dynami-
cally based on user progress (Vassileva and Deters,
1998). Markov decision (Durand et al., 2011), fuzzy
petri nets (Huang et al., 2008), and production rules
(Karampiperis and Sampson, 2005) are other tech-
niques that are applied in the LD field.
Although all above studies covered LD, none built
their models for a big set of objects. More recently a
LD approach based on graph theory (Durand et al.,
2013) has been tested on a large set of learning ob-
jects. The approaches mentioned compute the set of
possible paths and suggest one path in a single static
recommendation. When the user fails to proceed in
the recommended path, the recommender suggests
another one. Current LDRS lack a general evaluation
framework that enables the comparison of different
Since Long Term Recommender Systems are con-
cerned with guiding the user towards a goal, Learn-
ing Design principles and existing approaches are
highly relevant for their development. LTRS gener-
alize some of the LD principles to generate long term
recommmendations in any domain and not only to e-
2.3 Matrix Factorization (MF)
Recommender systems typically face the problems of
Sparsity and Scalability. The first one is related to
the fact that each user interacts with a very small frac-
tion of the items. The second is caused by the in-
creasingly high volumes of data found in practical
applications (Burke, 2002). Collaborative Filtering
(CF) techniques have been successful in dealing with
both problems. The majority of CF techniques are
based on the Matrix Factorization (MF) (Gillis, 2012).
Since we intend to generalize our strategy for differ-
ent scopes such as music (in case of music, we always
face the Sparsity), core of our strategy will be based
on matrix factorization approach.
Matrix Factorization (MF) discovers latent rela-
tions between users and items in a ratings matrix (Ko-
ren, 2008; Parambath, 2013). In the following we de-
scribe the basic MF setting. Suppose that R is a matrix
with size n × m (n user and m items) that entries are
ratings. By applying MF technique on the matrix:
R = A.B (1)
In (1), A represents a user matrix with size n × q
and B with size m × q portrays item matrix. Value
q represents the number of latent factors which are
learned from past responses of user. Interpretation of
factors is not easy and change tremendously depend-
ing on q selection. The following equation shows dot
product can be applied to predict the rate of user u to
item i:
= A
Minimization of regularized squared error for
known values in R is performed as training:
) (3)
The parameter (||A
+ ||B
) is used to avoid
over fitting by penalizing the high dimension param-
Recommender systems usually focus on satisfying
current users’ requirements and are evaluated as they
are. In this position paper, we argue for the impor-
tance of the study of LTRS that guide the users to a
predefined goal in item space. The users are guided
toward goal by generating a sequence of relevant rec-
ommendations through time. We intend to design and
develop a strategy to generate the recommendations
and a framework in order to evaluate the success of
our strategy. The proposed strategy is applicable in
different domains such as music, E-learning, etc.
To learn about long term interaction between users
and recommender systems, we are currently analyz-
ing the log data of a music recommendation system
in order to see how recommendations affect the evo-
lution of users (how they response to the recommen-
dations, their current activities and interests and etc.).
We collect activity data from the recommender ser-
vice (e.g. recommendations generated, recommenda-
tions followed). Later, when our strategic recommen-
dation method is running, we will introduce it into the
recommendation service and monitor the effects of its
usage with respect to the goals of this study. We will
also look for a second application set up in the area of
e-learning to explore more the long term goal recom-
The data feed defined in the previous step will be
used in a continuous streaming fashion to characterize
user behavior over time and to test the predictability
of users trajectories in the item space. The knowl-
edge acquired in this task will be important for the
development of our strategic recommendation algo-
rithm. It will also be of potential importance to other
researchers interested in user behavior and character-
The trajectory characterization step, we intend to
define a strategy that is able to learn from user activ-
ity and make a series of recommendations taking into
account well defined long term goals and user satis-
faction. Learning design principles will be used to
learn from the collected users’ data in order to gener-
ate more effective recommendations to guide users.
In addition, the recommendations will be based on
a matrix factorization approach which is detailed in
Section 2.3. We will use distance based reasoning to
make sense of the space of items and represent user’s
trajectories and goal in that space. We will also ex-
ploit other data to improve recommendations, namely
item features, and user-item interaction ratings (pref-
erence rating or test results in the case of e-learning).
RS researchers evaluate their proposals using the
following approaches:
Information Retrieval (IR) approaches such as
Precision and Recall
Machine Learning (ML) approaches such as
Decision Support System (DSS) approaches such
as customer satisfaction and user loyalty
Although many researchers used IR and ML mea-
sures in order to evaluate the recommenders (Yoo
et al., 2012), we need to continually measure users in-
teraction with system and DSS evaluation approaches
can provide more appropriate evaluation for LTRS.
Finally, we design appropriate evaluation mea-
sures and methodologies in order to assess the suc-
cess of the proposed methodology. This is a particu-
larly challenging task since, to be appropriate, evalu-
ation must be performed with live recommendations
on real situations. We will define goals for test users
and assess the success of the methodology in conduct-
ing users to the goals. Results will be compared with
a control group of users (A/B test). Offline, online
and user study are the methods that we intend to use
in evaluation phase.
Figure 2 provides a conceptual view of our idea.
It shows an item space (a set of objects with different
characteristics) that is included our target user who is
interested in specific type of objects (gray highlighted
area). Our strategy guides the target user toward goal
(green highlighted area) step by step while dynami-
cally assess how far the target user is from the tar-
get area (calculate the distance between the current
position of the target user and target area after each
recommendation). In this example, the goal of each
recommendation is to broaden the preference area of
target user until he starts to use the items in the tar-
get area (or decreasing the distance between the tar-
get user position area and target area (goal) by every
Figure 2: Conceptual view of LTRS.
Recommendation systems normally focus on the im-
mediate needs of users and are evaluated as such.
This is insufficient for long term goals recommenders.
Generating strategy for long term goals is of interest
in recommending learning resources to learn a con-
cept, and also when a company attempts to convince
users to buy certain products.
In this position paper, to face the Sparsity and
Scalability problems, we propose the use of matrix
factorization algorithm to handle both issues. Also, in
order to have more conductive and effective recom-
mendations, learning design principles and persuasive
technology will be utilized. Learning design is se-
lected to learn from the users’ activities and since the
LTRS’s recommendations must persuade the users,
we apply the persuasive technology.
To evaluate LTRS, we will require appropriate
methods to assess the success of strategic recommen-
dations, since existing measures such as Precision,
and Recall are clearly not enough. In any case, offline
and online evalution must be complemented with user
This study is supported by ”NORTE-07-0124-
FEDER-000059” which is financed by the North Por-
tugal Regional Operational Programme (ON.2 O
Novo Norte), under the National Strategic Reference
Framework (NSRF), through the European Regional
Development Fund (ERDF), and by national funds,
through the Portuguese funding agency, Fundac¸
para a Ci
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