=Paper=
{{Paper
|id=Vol-1367/paper-19
|storemode=property
|title=Towards a Model of Context-Aware Recommender System
|pdfUrl=https://ceur-ws.org/Vol-1367/paper-19.pdf
|volume=Vol-1367
|dblpUrl=https://dblp.org/rec/conf/caise/Jokste15
}}
==Towards a Model of Context-Aware Recommender System==
https://ceur-ws.org/Vol-1367/paper-19.pdf
Towards a Model of Context-Aware Recommender
System
Lauma Jokste
Information Technology Institute, Riga Technical University, Kalku 1, Riga, Latvia
lauma.jokste@rtu.lv
Abstract. Users often have difficulties to use large-scale information systems
efficiently because of their complexity. Additionally, these systems might be
context dependent. If these context dependencies are taken into account during
the system’s run-time phase, the most appropriate functionality might be
provided to users in the form of recommendations for each context situation.
The paper proposes to account for the context dependencies by using context
aware recommendations and outlines an approach for modeling such
recommendations. This approach is based on methodological foundations of
Capability Driven Development. The paper discusses a motivational case for
developing context aware recommendations and presents the initial method for
recommendation modelling.
Keywords: Recommender Systems, Recommendation Modeling, Context-
Aware recommendations, Capability Metamodel.
1 Introduction and Related Work
Large-scale information systems (IS) are subject to dynamically changing
circumstances in the IS delivery phase. The current situation can be described by
different context data defined as “any information that can be used to characterize
the situation of an entity. An entity is a person, place, or object that is considered
relevant to the interaction between the user and the application, including the user
and the applications themselves” [1]. The context data can be taken into account in IS
delivery thus increasing the usability and user satisfaction.
Complex and extensive IS might reduce the user satisfaction and, if possible, users
might choose alternative ways of completing their tasks. For example, citizens are
avoiding using public e-services and are favoring physical services. Recommender
systems are widely used in order to improve the usage of software and tools. They
provide suggestions by recommending the items that users might likely be interested
in [2]. Recommender systems are increasingly popular. Many recommender systems
focus on improving and evaluating the collaborative-filtering technique [3]. They use
internal information about historical user activity, user profile information and other
to match users with recommended items [4].
Gradually, the recommender systems start to use more varied data and data
sources, for example, social network data [5][6][7]. In some cases the context
information might be relevant to calculate the most appropriate recommendations for
users by using context-aware recommendation systems [8]. Recent investigations
present location-based [9] and weather-dependent [10] recommendation algorithms
and methods. Discovering suitable context data is still a challenge in recommender
systems evaluation [11]. The proposed approach in this paper includes the usage of
different context data that can be retrieved from both internal and external data
sources. Context processing includes not only reading the context data, but also
context data analysis which helps to predict the context data and user behavior.
The recommender systems are usually item oriented and suggest the items in which
users would be potentially interested in [12]. For instance, e-commerce sites such as
Amazon.com recommend items users would be likely to buy [13], while content
based systems recommend things based on textual analysis, e.g. in research area,
citations can be recommended for the research by analyzing words in research papers
[14]. In order to improve the recommender algorithms, hybrid recommender systems
are developed by combining different recommendation algorithms and methods into
one information system [15]. The approach proposed in this paper assumes that
recommendations could be any kind of software entity and examples of
recommendations include suggestions to execute a function, procedure, workflow or
to perform data processing operations.
This paper presents the recommendation system proposal which starts with
recommendation modeling. Different kind of context information can be used as an
input to the recommendation module. The modeling phase is considered as significant
since models can help to deal with complexity and are easy perceptible for
stakeholders without any specific IT skills [16]. Recommendation modeling includes
specifying a set of business rules that help to define the software entities context
dependencies and appoints which recommendations should be run in each contextual
situation.
Nowadays variability in IS delivery becomes more and more important. When
business processes change, the software supporting these processes should be adjusted
accordingly in order to fulfill the organizations goals [17]. Competitive software
should be able to deal with the variability with minimal efforts. Changes in business
processes can be affected by internal and/or external context. Adjusting software to
changes in form of user recommendations by integrating Recommendation module in
existing software allows to deal with variability without an important effort because
recommendations can be easily changed in recommendation module without changing
underlying software.
This paper analyses the potential of using context-aware recommendations and
proposes an approach for modeling context aware recommender systems. The
modeling approach is based on the Capability Driven Development (CDD) method
used in development of adaptive systems [18]. The potential of using context-aware
recommender systems is analyzed by exploring a use case from the e-government
domain described in section 2. Section 3 provides an overview of the recommendation
modeling method including a small representative example of recommendation
modeling. Section 4 presents the conclusions and future work.
2 Motivational case
In order to justify development of context aware recommender systems, context
dependencies in large scale IS are analyzed by using the case of the public electronic
finishing service E-Loms, part of the municipal e-service platform
www.epakalpojumi.lv developed by the Latvian company ZZ Dats Ltd. Currently, E-
Loms provides several functions: to check information about lakes and rivers; to buy
fishing licences online; to register the catch etc. E-service users’ statistics (see the
following figures) show that the number of licences sold varies in a wide range
meaning that the usage of this service is affected by different circumstances. Several
context elements affecting the e-service usage have been retrieved during the e-
service usage analysis.
Assumption 1: E-service usage depends on the current and predicted air
precipitation level. Fig. 1 shows the number of licences sold in September 2014 and
the level of air precipitation in September 2014. There have been two pronounced rain
periods and during these periods the number of fishing licences sold has rapidly
decreased, thus the service usage dependents upon current and predicted air
precipitation level.
Fig. 1. E-service usage and air precipitation level dependencies
Assumption 2: E-service usage depends on the current and predicted air
temperature. Fig. 2 shows the number of fishing licences sold and the average air
temperature in February 2014. The same periods are highlighted in both charts.
Periods with the lowest air temperature and the sharpest drops in temperature match
with periods in which the largest numbers of fishing licences have been sold. In
winter, ice fishing is very popular but it requires low air temperatures. This means e-
service usage is highly dependent on the air temperature.
Assumption 3: E-service usage depends on calendar events. Analysis of e-service
usage statistics in 2014 brings forward the following regularities:
The beginning of January – many fishermen buy their yearly fishing licence;
1st of May – fishing season opening – in the end of April and the beginning
of May the number of licences sold has rapidly increased;
In the beginning of June the number of fishing licences sold has rapidly
increased marking the beginning of the summer and vacations periods.
The number of licenses sold is rapidly increasing on Fridays and Saturdays.
Fig. 2. Number of sold fishing licenses and average air temperatures in February 2014
The examples suggest that the usage of the E-Loms e-service is highly affected by
different dynamical context data. When monitoring, predicting and taking into
account the run-time context data, the e-service delivery might be improved by
generating context aware recommendations for the service users.
There are many other context data that can be obtained, stored and exploited in
order to apply recommendations that help users to navigate in large service catalogs
or recommend users potentially the most important functions in each context
situation. This could take the recommendations to a new level where context
dependencies could be defined for software entities and the execution of other
software entities can be provided to users in the form of recommendations.
3 Context-aware Recommendation Modeling
Usually recommendations are item-oriented where items are suggested based on user
profile and historical user activity information which is mined from usage logs [19].
The use case example demonstrated in Section 2 proves that not only items can be
recommended, but also different software entities can be used as recommendations.
The conceptual basis of context-aware recommendation algorithm design and
development is based on the Capability metamodel [18] which is developed within
the FP7 project “CaaS: Capability as a Service” and is a part of the Capability Driven
Development approach. Capability term is important in recommendations modeling
because context processing and recommendations application relates to IS ability to
deliver a certain value to a user in changing context circumstances. This section
presents the Capability metamodel which is expanded in order to fulfill the needs for
modeling context-aware recommendations.
The specific aspects relevant for context-aware recommendation modeling which
are added to the Capability metamodel are marked in a darker color in Fig. 3.
From the recommendation’s perspective, the Software Entity is the detailed (i.e.,
executable) level of Capability and each Software Entity has a Context Set which
defines the context information that affects the Software Entity delivery. The term
Software Entity describes different executable artefacts and can be either software as
a whole or a single workflow, procedure, function, job etc.
Recommendations conform to the definition of patterns which are “reusable
solutions for reaching business Goals under specific situational contexts.” [18] They
are reusable solutions that can be applied to the Software Entity in a certain run-time
context situation with the purpose of improving the usage of the Software Entity. The
recommendations are user-oriented and improve the user satisfaction with a software,
service or software entity.
class Recommendations Metamodel
Measurable set for Context
Property 1..* 0..1 adjustment rule
0..* 0..*
measured by conforms
0..1
0..*
Context Context contains design for
Context Set Software Entity contains Capability
Type 1 Element 1 1..* 1 0..*
* 0..* 1
1
has 0..*
1..*
supports
Context Capability
Element Value Adjustment
Rule Recommendation
1..* 1..* 0..* 1 Text
obtained from applies 0..*
consists of initiates
1..* 1 1..* 1 1 1
Context Source
Context fits for Recommendation has Recommendation
Situation 1..* Pattern Pattern Type
1..* 1 1
1 0..*
consists of
Fig. 3. Capability metamodel for modeling context-aware recommendations
A Recommendation Pattern can be applied automatically (e.g. automatic Software
Entity highlighting) or recommended to a user with an informative notification which
provides a choice to the user to run the recommended Recommendation Pattern. In
the second case, the Recommendation Text should be defined as a separate class in
the metamodel that marks separately modifiable text without changing the
Recommendation Pattern itself. Each Recommendation Pattern can have multiple
notification text values and the appropriate value for each context situation is defined
in a Capability Adjustment Rule. In the example model (Fig. 6), the Recommendation
Pattern is for instance a classificatory value ‘yearly license’ and the Recommendation
Text recommends users to choose a licence type ‘yearly license’ instead of ‘one day
license’.
Capability
Adjustment Rules
repository
Context Capability
Adjustment Adjustment Rules
Rules repository repository Execulte
Internal/ Apply
External data
Capability
Rule found Recommendation
base Adjustment
Pattern
Compliance = True rule
Search
Capability
Adjustment
Check
Context rule
compliance to Rule not found
data Context
monitoring Adjustment
Rules
Compliance = False
Fig. 4. General recommendation algorithm execution business process model
Context Adjustment Rules define the context ranges which affect the Software
Entity while Capability Adjustment Rules initiate the recommendation which is
appropriate for the certain run-time Context Situation. The form of Capability
Adjustment Rules should be decided, for instance rules can be written as IF/THEN or
Event-Condition-Action rules. The Recommendation Pattern Type specifies the type
of recommendation, e.g. a function, procedure, textual notification, etc. The Context
Source serves as a reference to the source from which the context data is obtained.
Based on a Recommendation model which is modelled according to proposed
metamodel, a Recommendation module can be built. In Fig. 4 a general
recommendation module execution process is given. The process includes concepts
from the Capability metamodel. Context data monitoring is represented as a
subprocess which will be specified in further research by taking into account different
context sources that can be used to obtain context information relevant to IS delivery.
In Fig. 5 and 6 an example of a recommendation model is given.
class E-Loms Recommendations Model
Measurable Property:
Impact of Calendar
event Capability: E-Loms
Service Promotion
Measurable Property: Context Element:
Date of Calendar event Calendar event
Context Adjustment Rule: Context Set: Licence Software Entity:
Licence purchase Measurable Property: purchase procedure Licence purchase
Context Element:
procedure is dependant of Degrees, C procedure
Air temperature
value(Air temperature) =<0
or >=20
Measurable Property:
Context Adjustment Rule: Number of purchased
Licence purchase procedure licences in particular
is dependant of lake/river
value(Number of purchased Context Element:
licences in particular Purchase history
lake/river)=<1 Measurable Property:
Type of purchased
licences in particular
lake/river
Fig. 5. Context modeling part of the E-Loms recommendation model example
class E-Loms Recommendations Model
Recommendation
Text: 'Want to buy a
yearly licence?'
Recommendation Recommendation R1:
Type: Value from licence classification
classificatior value 'Yearly licence'
Software Entity:
Licence purchase
procedure
Recommendation Recommendation R2:
Type: Textual Inform about air
notification temperature
Recommendation Text:
'Warning:
air temperature will be
below zero!'
Fig. 6. Recommendation modeling part of the E-Loms recommendation model example
The Context Situation is a run-time element and cannot be modeled during the
design phase. For instance, if a user is buying a one-day license in a lake where he
already has bought one-day licenses, then another value from the classificatory can be
recommended – ‘a yearly license’. Recommendations can be run by a Capability
Adjustment Rule, e.g. If number of bought licenses in lake X >=1, THEN run
recommendation R1.
4 Conclusion and Future work
In this paper we discuss the usage of the Capability metamodel in the design of
context-aware recommendations for software entities in order to increase user
satisfaction. The example demonstrated in the paper proves the potential of generating
recommendations based on run-time context data.
Current recommendation algorithms and methods are mostly item-oriented and
based on a limited amount of data (user profile data, user activity, similarities between
users and items etc.). The delivery of many software and software entities is affected
by different context information. The achievement of business goals can be improved
by applying context aware recommendations in software delivery.
The recommendation modeling method would allow designing modifiable
recommendations more efficiently. In the Recommendation module,
recommendations can be modified by defining or changing Capability Adjustment
and Context Adjustment Rules, recommendation values, or modifying
recommendations in the recommendation repository. Once the module is built,
stakeholders without specific IT skills should be able to manage and maintain the
recommendations.
This paper presents the initial proposal for context-aware recommendations
modeling. The context-aware recommendation modeling and designing method
should be improved and developed in detail in following aspects:
The form for defining the Context Adjustment Rules and the Capability
Adjustment Rules should be specified;
The context processing subprocess should be specified;
The technical solution for developing and implementing the recommendation
module should be developed;
The recommendation modeling method and recommendation module should
be validated.
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