PROVIDING RECOMMENDATIONS IN AN AGENT-BASED

TRANSPORTATION TRANSACTIONS MANAGEMENT

PLATFORM

Alexis Lazanas, Nikos Karacapilidis and Yiannis Pirovolakis

Industrial Management and Information Systems Lab, MEAD

University of Patras, 26504 Rio Patras, Greece

Keywords: Recommendation techniques, recommender systems, transportation, web-based systems, intelligent agents.

Abstract: Diverse recommendation techniques have been already proposed and encapsulated into several e-business

systems aiming to perform a more accurate evaluation of the existing alternatives and accordingly augment

the assistance provided to the users involved. Extending previous work, this paper focuses on the

development of a recommendation module for transportation transactions purposes and its integration in a

web-based platform. The module is built according to a hybrid recommendation technique, which combines

the advantages of collaborative filtering and knowledge-based recommendations. The proposed technique

and supporting module enable customers to consider in detail alternative transportation transactions

satisfying their requests, as well as to evaluate such transactions after their completion.

1 INTRODUCTION

Transportation management involves diverse

decision making issues, which are basically related

to the choice of route and carrier selection. Such

issues raise due to the differentiation between the

dispatcher’s preferences (e.g. cost limitation, loading,

and delivery dates) and the carrier’s service

resources (e.g. transportation media, itinerary, and

capacity). The matching of the above preferences

and services cannot be easily handled manually, as

in most cases a plethora of alternative options exist,

while time and money limitations are ubiquitous.

Therefore, the field of transportation management

requires quick and cost-effective solutions to the

customers’ demands for distribution and shipping

operations.

This paper extends our previous work on the

exploitation of software agent technology in

transportation management (Karacapilidis et al.,

2006; Lazanas et al., 2005). More specifically, we

have addressed analysis, design and implementation

issues raised during the development of an

innovative agent-mediated electronic marketplace,

which is able to efficiently handle transportation

transactions of various types. Agents of the proposed

system represent and act for any user involved in a

transportation scenario, such as customers who look

for efficient ways to ship their products and

transport companies that may - fully or partially -

carry out such requests, while they cooperate and get

the related information in real-time mode. Our

overall approach is based on flexible models that

achieve efficient communication among all parties

involved, coordinate the overall process, construct

possible alternative solutions and perform the

required decision-making. In addition, the

supporting web-based system is able to handle the

complexity that is inherent in such environments,

which is mainly due to the frequent need of finding a

“modular transportation solution”. To further

explain this concept, consider the case where a

customer wants to convey some goods from place A

to place B, while there is no transport company

acting directly between these two places. Supposing

that two available carriers X and Y have some

scheduled itineraries from A to C and from C to B,

respectively, it is obvious that a possible solution to

the above customer’s request is to involve both X

and Y and fragment the intended overall itinerary to

the related sub-routes. It is also noted that these

carriers may be associated with diverse

transportation means, such as trains, trucks, ships

and airplanes. Our system is able to manage all the

necessary freighting and fleet scheduling processes

in wide-area transportation networks. Its agents

Lazanas A., Karacapilidis N. and Pirovolakis Y. (2006).

PROVIDING RECOMMENDATIONS IN AN AGENT-BASED TRANSPORTATION TRANSACTIONS MANAGEMENT PLATFORM.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - SAIC, pages 87-92

DOI: 10.5220/0002461900870092

 SciTePress

cooperate upon well-specified business models,

which may efficiently carry out diverse processes.

This paper focuses on the features and

functionalities of a new module integrated in the

above system, namely the recommendation module,

which aims at enhancing the quality of the

associated decisions. Recommender systems have

been described as systems that produce

individualized recommendations as output or have

the effect of guiding the user in a personalized way,

in environments where the amount of on-line

information vastly outstrips any individual’s

capability to survey it (Burke, 2002). Alternative

techniques have been also proposed in the literature

in order to handle the above issues. Having

thoroughly considered their pros and cons, our

approach follows a hybrid recommendation

technique.

The remainder of this paper is structured as

follows. Section 2 discusses approaches and related

work from the area of recommender systems.

Section 3 describes the basic aspects of our

approach, namely transportation plan selection,

alternative solution evaluation and recommendation

methodology. Section 4 deals with the evaluation of

a transaction and the exploitation of agent

technologies. Finally, Section 5 concludes the paper.

2 RECOMMENDER SYSTEMS

Recommender systems apply data analysis

techniques to assist users finding the items they need

by producing a predicted likeness score or a list of

top-N recommended items. Two of the most widely

adopted recommendation techniques are

Collaborative Filtering (CF) and Knowledge Based

Recommendation (KBR), each one possessing its

own strengths and weaknesses. Collaborative

Filtering (CF) (Resnick et al., 1994) is the most

commonly used recommendation technique to date.

The basic idea of CF-based algorithms is to provide

item recommendations or predictions, based on the

opinion of other like-minded users. In a typical CF

scenario, there is a list of m users U = {u

,…,u

}

and a list of n items I = {i

,…,i

}. Each user u

associated with a list of items I

, for which the user

has expressed his/her opinion. Opinions can be

explicitly given by the user as a rating score,

generally within a certain numerical scale, or can be

implicitly derived from transaction records, by

analyzing timing logs, mining web hyperlinks and so

on. For a particular user U

, the task of a

collaborative filtering algorithm is to find an item

likeness that can be of two forms:

• Prediction: this is a numerical value, P

a,j

expressing the predicted likeness of item i

does not belong in I

) for the user. The

predicted value is within the same scale

(e.g. 1 to 5) as the opinion values provided

by U

• Recommendation: this is a list of N items I

is a subset of I) that the user will like

most (the recommended list must contain

items not already selected by the user). This

outcome of CF algorithms is also known as

Top-N recommendation. (Sarwar et al.,

2000).

On the other hand, Knowledge-Based

Recommendation attempts to suggest objects based

on inferences about a user’s needs and preferences.

In some sense, all recommendation techniques could

be described as doing some kind of inference.

Knowledge-based approaches are distinguished in

that they utilize functional knowledge: that is, they

have knowledge about how a particular item meets a

particular user need, and can therefore reason about

the relationship between a need and a possible

recommendation. The user profile can be any

knowledge structure that supports this inference. In

the simplest case, as in Google (www.google.com),

it may simply be the query that the user has

formulated. The Entrée system and several other

recent systems (for an overview, see (Schmitt &

Bergmann, 1999)) employ techniques from case-

based reasoning for knowledge-based

recommendations.

The knowledge used by a knowledge-based

recommender system can also take many forms.

Google uses information about the links between

web pages to infer popularity and authoritative value

(Brin & Page, 1998). Entrée uses knowledge of

cuisines to infer similarity between restaurants.

Utility-based approaches calculate a utility value for

objects to be recommended; in principle, such

calculations could be based on functional

knowledge. However, existing systems do not use

such inference mechanisms, thus requiring users to

do their own mapping between their needs and the

features of products, either in the form of preference

functions for each feature, as in the case of Tête-à-

Tête, or answers to a detailed questionnaire, as in the

case of PersonaLogic (Burke, 2002). Knowledge-

based recommender systems are prone to the

drawback of all knowledge-based systems: the need

for knowledge acquisition. More specifically, there

ICEIS 2006 - SOFTWARE AGENTS AND INTERNET COMPUTING

are three types of knowledge that are involved in

such a system:

• Catalog knowledge: Knowledge about the

objects being recommended and their

features. For example, the Entrée

recommender should know that “Thai”

cuisine is a kind of “Asian” cuisine.

• Functional knowledge: The system must be

able to match the user’s needs with the

object that might satisfy those needs. For

example, Entrée knows that a need for a

romantic dinner spot could be met by a

restaurant that is “quiet with an ocean

view”.

• User knowledge: To provide good

recommendations, the system must have

some knowledge about the user. This might

take the form of general demographic

information or specific information about

the need for which a recommendation is

sought.

Of these knowledge types, the last one is the

most challenging, as it is, in the worst case, an

instance of the general user-modeling problem.

Despite this drawback, knowledge-based

recommendation has some beneficial characteristics.

It is appropriate for casual exploration, because it

demands less from the user compared to the utility-

based recommendation. Moreover, it does not

involve a start-up period during which its

suggestions are of low quality. On the other hand, a

knowledge-based recommender cannot “discover”

user niches, the way collaborative systems can.

However, it can make recommendations as wide-

ranging as its knowledge base allows.

3 OUR APPROACH

3.1 Transportation Plans and

Evaluation of Alternative

Solutions

The recommendation procedure adopted in our

approach is highly associated with the selection (by

the user) of the appropriate “transportation plan”

(see Figure 1). A transportation plan typically

defines the user preferences for the upcoming

transactions. The five alternative plans offered are:

• Express

• Economic

• Safe

• Dependable

• User Defined

It can be easily observed that each of the first

four plans declares a specific tension in the

recommendation strategy to be followed by the

system, in that it either minimizes the overall

duration or cost (first two plans) or it retains a high

level of safety or dependability (third and fourth

plans) of the suggested itineraries. The last choice

offers the possibility for a customized plan

definition. A user defined plan may combine

parameters from all the above four plans. The

selection of one of these plans will influence the

recommendations processes of our approach for the

particular user.

Figure 1: User request for a transaction.

Having constructed all possible transportation

solutions, our approach proceeds to the evaluation

phase. In this phase, our approach’s Decision_Maker

agent performs two distinct tasks. First, a refinement

of the set of alternative solutions constructed takes

place, by excluding solutions that do not comply

with the customer’s requirements (the algorithm

developed for the construction of alternative routes

takes into account only the duration and cost

parameters as upper bounds of the proposed

solutions). In this phase, a set of predefined rules are

employed to exclude the alternative solutions that do

not correspond to the specific freight

transportation’s requirements and customer

preferences. For instance, if the customer had chosen

the “Safe” transportation plan, the following set of

rules will be deployed:

for each constructed_solution

{

if (safety_level < AVERAGE) OR

((safety_level = AVERAGE) AND

PROVIDING RECOMMENDATIONS IN AN AGENT-BASED TRANSPORTATION TRANSACTIONS

MANAGEMENT PLATFORM

(dependability_level < LOW)) then

discard constructed_solution;

else if (safety_level >= HIGH) OR

((safety_level = AVERAGE) AND

(dependability_level >= LOW)) then

solution ( constructed_solution;

}

Table 1 presents the constraints to be met for

each transportation plan (for the User Defined plan,

this process takes into account the constraints set by

the user). In all cases, solutions that do not satisfy

these constraints are discarded.

Table 1: Selection Criteria for each plan.

Plan

Cost Duration Safety

Dependa-

bility

1 Any Min Any Any

2 Min Any Any Any

3 Any Any >Average >=Low

4 Any Any >= Low > Average

5 UD

UD UD UD

Plans 1 to 5 correspond to: Express, Economic, Safe,

Dependable and Hybrid.

User Defined Criterion

3.2 A Methodology for the Selection

of Alternative Route Paths

In order to construct optimal and sub-optimal

solutions, our approach uses an elaborated version of

Dijkstra’s shortest path algorithm (Crauser et al.,

1998). The majority of shortest path algorithms in

the literature uses a bidirectional, single-weighted

graph to represent a connected set of vertices (V

)

through a number of arcs A

(from V

to V

). Our

algorithm takes into consideration each A

and its

correspondent weight (w

) in order to produce a

route path from a starting point (S) to an ending

point (E) that minimizes the total weight (w

). The

complexity in our approach consists in the presence

of a pair of factors that affect each arc’s weight,

namely the cost and the duration. Due to the fact that

there exist two weights for each arc, we confronted

the problem of unifying these weights into a single

one, in order to proceed with the ranking of the

solutions.

More specifically, each arc’s A

weight (W

)

consists of a cost weight (

cost-ij

) and a duration

weight (

duration-ij

). Obviously, W

= W

cost-ij

duration-ij

Having defined the total weight for each arc we

encountered the problem of adding these two

parameters that are measured in different units

(Euros and hours, respectively). This problem was

confronted by applying a normalization technique

that divided both the cost and duration terms of an

route with the correspondent maximum cost and

duration. It is:

duration-ij

= duration

/max(duration)

cost-ij

= cost

/max(cost)

Another issue that came up after the weight

normalization procedure concerned the solutions

ranking. To address this problem, our approach

provides the user with different solutions by using a

pair of weight coefficients (

costCoef and

durationCoef) and calculating solutions

corresponding to alternative combinations of the

weights of the cost and duration criteria (see Figure

2), according to the formula:

=(costCoef * w

cost-ij

) +

(durationCoef * w

duration-ij

)

Figure 2: Coefficients’ variation.

This process is described in pseudo-code below:

{

costCoef Å 0.0;

durationCoef Å 1.0;

step ( 0.0;

for each step calculate

{

weight[i][j] ( costCoef*Wcost +

durationCoef * Wduration;

costCoef ( step;

durationCoef = 1-step;

}

perform shortest path algorithm;

step ( step + 0.1;

}

The outcome of this process is then provided to

the user. An instance of the related system interface

is shown in Figure 3.

ICEIS 2006 - SOFTWARE AGENTS AND INTERNET COMPUTING

3.3 Recommendation Methodology

The recommendation procedure begins immediately

after the ranking of the alternatives. It is a complex

process which is carried out in three basic stages: the

evaluation of the carriers and the transactions data,

the exploitation of transaction data through a data

mining process, and the recommendation

methodology selection or synthesis. At the

beginning of the process, the system stores all the

appropriate data that are submitted by the user and

are related with scheduled or completed transactions.

These data are of significant importance and will be

further exploited by the data mining process.

Moreover, in this stage the user evaluates the

carrier(s) involved in a transaction through an

interface since the evaluation process concerns

assigning a score to each carrier (this is discussed in

more detail in Section 4.1).

Figure 3: Solution ranking produced by the system.

The second stage of recommendation concerns

the data mining process. At this stage, transactions

data are gathered through knowledge construction

processes (Cho et al., 2002). Although important,

the description of these processes goes out of the

scope of this paper. We only mention here that

knowledge construction in our case refers to the

formulation of rules that could be used when a

knowledge based recommendation strategy is

performed.

The last phase of the recommendation refers to

the selection or synthesis of the appropriate

recommendation technique. This objective will be

reached through the definition of well structured

rules that will be applied for each transaction. The

recommender agent of our system takes the initiative

to look for the most appropriate recommendation

technique. For example, for a particular itinerary

from point i to j, taking into consideration that the

customer has selected a certain plan, a rule for the

specific itinerary could recommend a carrier that is

different than the one suggested by the CF

technique, based on the carriers’ evaluation process

described earlier in this section.

4 IMPLEMENTATION ISSUES

The recommender module has been integrated into

our platform as an add-on tool and has been

thoroughly tested. After the completion of a

transportation transaction, the customer is able to

evaluate it by providing his/her opinion about the

carrier(s) who were involved. Since transactions

handled by our platform often concern modular

solutions, multiple carriers may be involved. Thus,

the rating procedure concerns each individual

carrier.

Figure 4: The carriers’ evaluation interface.

As shown in Figure 4, a customer expresses

his/her position for a completed transaction by

selecting a value for each feature, following a typical

Likert scale (very low, low, average, high, very

high). Each value is stored in the appropriate table in

the systems SQL Server database. This rating is very

helpful during the main phase of the

recommendation process presented in Figure 5,

which takes place after the ranking of the alternative

solutions described earlier. As shown in the figure,

for the itinerary “London–Prague” the customer has

selected one of the proposed routes provided by the

system (see Figure 3). After selecting the specific

route, the customer may request a recommendation

for the involved carriers. The system provides

different types of recommendations features such as:

PROVIDING RECOMMENDATIONS IN AN AGENT-BASED TRANSPORTATION TRANSACTIONS

MANAGEMENT PLATFORM

an average score ranking, top-10 carriers, “red card”

carrier (carriers with under average scoring), and a

“suggestion” functionality. Each suggestion made by

the system is accompanied by a prediction status

label P

= {critical, acceptable, indifferent}, which

refers to the significance level of the system’s

suggestion. In the example shown in Figure 5, the

suggestion is concerned “critical” because the

system retains some “negative” knowledge for the

carrier under consideration.

Figure 5: The recommendation module of the system.

For the implementation of the proposed

recommendation technique, a recommender module

has been developed using agent and data

warehousing technologies. More specifically, a new

agent, namely the Recommender Agent, has been

implemented in order to coordinate the overall

process of recommendation and has been assigned

with two main tasks:

• The recommendation technique selection,

in that the agent is capable of switching

among alternative recommendation

algorithms, depending on the characteristics

of each transaction. The agent carries out

data mining tasks and generates the

appropriate rules or models.

• The characterization of each carrier who is

involved in a transportation transaction and

the recommendation of alternative carriers

capable of fulfilling a particular

transportation request.

Through these formally modeled tasks, the

recommender agent provides continuous assistance

to customers, while it remains active and capable to

adapt its “behavior” into a rapidly changing

environment.

5 CONCLUSIONS

This paper presents our approach on the integration

of hybrid recommendation techniques into an agent–

based transportations transaction management

platform. We proposed a hybrid approach that

combines different recommendation techniques, in

order to provide the user with more accurate

suggestions. The overall process is coordinated by a

recommender agent, who is responsible of

performing multiple tasks. The presence of the agent

guarantees that the user will be provided with

continuous recommendation, dynamic update and

recommendation techniques synthesis.

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