Koba4MS: Knowledge-based Recommenders

for Marketing and Sales

Alexander Felfernig

Computer Science and Manufacturing, University Klagenfurt,

Universitätsstrasse 65-67, A-9020 Klagenfurt, Austria,

Abstract. Due to the increasing size and complexity of products offered by on-

line stores and electronic marketplaces, the identiﬁcation of solutions ﬁtting to

the wishes and needs of a customer is a challenging task. Customers can dif-

fer greatly in their expertise and level of knowledge w.r.t. the product domain

which requires sales assistance systems allowing personalized dialogs, explana-

tions and repair proposals in the case of inconsistent requirements. In this context,

knowledge-based recommenders allow a ﬂexible mapping of product, marketing

and sales knowledge to the formal representation of a knowledge base. This pa-

per presents the knowledge-based recommender environment Koba4MS which

assists customers and sales representatives in the identiﬁcation of appropriate so-

lutions. Based on application examples from the domain of ﬁnancial services,

basic Koba4MS technologies are presented which support the effective imple-

mentation of customer-oriented sales dialogs.

1 Introduction

Buying complex products (e.g. ﬁnancial services, computers, etc.) is still a challenging

task since many organizations offer simple query interfaces under the assumption that

customers know the technical details of the offered set of products [1]. Recommender

technologies [1,2,3,4,5,6,7,8,9] improve this situation by providing solution alternatives

for the customer which are automatically derived from a set of customer requirements.

There are three basic approaches to the implementation of recommender applications.

Collaborative Filtering [4,6,7] is based on the concept of storing preferences of a large

set of customers. Based on the assumption that human preferences are correlated, rec-

ommendations given to a customer are derived from preferences of a group of customers

with similar interests, i.e. no deep knowledge about product properties is needed. Sim-

ilarly, using Content-based Filtering [3,5], products are described by a set of keywords

(categories) which are stored in a customer proﬁle in the case that a customer buys a

certain product. The next time, the customer enters the system, the stored preferences

are used for identifying additional products which are assigned to similar categories.

Additionally, there exist a number of approaches combining these basic approaches

in order to gain an improved quality of the resulting solutions (see e.g. [10]). Finally,

Knowledge-based Recommender applications (advisors) [1,2] exploit deep knowledge

about the product domain in order to determine solutions exactly ﬁtting to the wishes

and needs of the customer. When selling complex products such as ﬁnancial services, a

Felfernig A. (2005).

Koba4MS: Knowledge-based Recommenders for Marketing and Sales.

In Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces, pages 164-174

DOI: 10.5220/0001422601640174

 SciTePress

customer’s taste is not of primary concern - primarily solutions and explanations must

be correct in every case (e.g., due to legal regulations), i.e., must correspond with the

requirements articulated by the customer (see Section 3.1 for the repair of inconsistent

customer requirements). This requirement can only be met by explicitly representing

product, marketing, and sales knowledge [11,12], i.e. Knowledge-based Recommender

applications (advisors) are the natural choice in this context.

Fig.1. Overall architecture.

In the following we give an overview of the major technologies implemented within

the Koba4MS

environment, a domain-independent tool designed for the development

of knowledge-based advisors. The major difference between Koba4MS and other know-

ledge–based recommender systems [2] is the inclusion of model-based diagnosis [13,14]

and personalization techniques [1] which improve the effectiveness of advisor devel-

opment as well as the interaction with the advisor. For example, a graphical devel-

opment and test environment makes the implementation of advisors feasible for non-

programmers, furthermore intelligent diagnosis and repair techniques actively support

customers in situations where no solution could be found. Koba4MS can be applied

in the following scenarios. Firstly, similar to traditional sales channels, improved sales

assistance by improving the accessibility of a product assortment on a companies Web

page generates added value for customers. On the one hand advisors allow an intu-

itive access to complex products for customers, on the other hand sales representatives

are relieved from routine advisory jobs. Secondly, sales representatives interact with

advisors when talking with the customer, where guided dialogs provide questions and

explanations focusing on the customers wishes and needs. Throughout the paper we

provide real-world examples from the ﬁnancial services domain which is our leading

application domain. Financial service advisory is a knowledge-intensive task which in

many cases overwhelms customers as well as sales representatives. Therefore ﬁnancial

service providers ask for tools providing an intuitive access to their product assortment.

The remainder of the paper is organized as follows. In Section 2 we present the

Koba4MS recommender environment. In Section 3 we present examples for the us-

age of AI technologies which allow the implementation of knowledge-based advisors

Knowledge-based Advisors for Marketing and Sales is a project funded by the Austrian Re-

search Fund (agreement number FFF-808479).

Products of ﬁnancial service providers cover different areas of interest such as investment

decisions, ﬁnancing, pension, life insurance, etc.

165

(constraint satisfaction, model-based diagnosis and test, personalization, knowledge ac-

quisition). Finally, Section 4 presents experiences from commercial advisor projects.

2 Koba4MS Environment

The Koba4MS environment (see Figure 1) provides comprehensive assistance for cus-

tomers and sales representatives by supporting guided and personalized dialogs allow-

ing an intuitive access to an assortment of complex products. In this context Koba4MS

can be used for the following purposes.

– formalization of product, marketing and sales knowledge by non-programmers.

– testing/debugging knowledge bases in order to identify faulty constraint deﬁnitions.

– checking customer requirements for consistency and (in the case of inconsistencies)

supporting a corresponding error handling.

– matching customer requirements to product properties, i.e. calculating a solution.

– diagnosing and repairing a set of inconsistent customer requirements, i.e. proposing

minimal changes which allow the retrieval of a solution.

– explaining solutions in order to increase the conﬁdence of the customer.

Koba4MS technologies are used in application domains such as ﬁnancial services, dig-

ital cameras, cigars, computers, services in public administration etc. In the ﬁnancial

services domain Koba4MS technologies are applicable for the following reasons.

– Solutions must be objective, correct and explainable which makes approaches such

as Collaborative Filtering or Content-based Filtering not the best choice.

– Typically, ﬁnancial service providers want to develop advisors autonomously, i.e.

knowledge representation formalisms are needed which allow the development of

recommender knowledge bases for non-programmers (this is supported by graphi-

cal knowledge acquisition, model-based debugging and testing).

– Intelligent explanation, debugging, and repair mechanisms as well as automated

test case generation are using model-based knowledge representations, i.e. deep

knowledge about the application domain must be available (which is not available

in Collaborative Filtering or Content-based Filtering approaches).

– Financial services recommendation is a complex task with a large number of con-

straints and possible solutions. In this context, knowledge-based approaches can

signiﬁcantly reduce efforts related to advisor development and maintenance.

Similar reasons motivate the application of knowledge-based advisors in other applica-

tion domains such as online-selling of computers, digital cameras, etc.

2.1 Overall Architecture

Koba4MS product knowledge bases and process deﬁnitions are developed and main-

tained using a Development and Test environment (Koba4MS Designer and Process

Designer). Products are deﬁned within Koba4MS Designer or imported from exter-

nal systems using an XML interface (for details see [1]). In the following advisors

166

are automatically generated and made available for customers (e.g. online-stores, e-

marketplaces, etc.) and sales representatives (e.g. intranet applications or installations

on notebooks of sales representatives), where Koba4MS Server supports the execution

of advisory sessions (Runtime Environment).

2.2 Development & Test Environment

Koba4MS Designer. Koba4MS Designer is a graphical development environment for

knowledge-based recommenders. It is based on Java Web Start which provides a browser-

independent architecture for deploying Java-2 based applications on a client. The con-

cepts implemented in Koba4MS are based on long-term AI research in the area of

knowledge-based conﬁguration and personalization [1,15,13,11,16]. Koba4MS Designer

supports the design of advisors, where the relevant set of product- and customer prop-

erties is identiﬁed and transformed into a recommender knowledge base [11,12]. Such

a knowledge base consists of the following parts (see Figure 2).

– product properties are structural descriptions of the provided products (e.g. life in-

surances can be characterised by the possible length of life assurance policies, pre-

miums of life assurance policies, links to additional product documentation, etc.).

– customer properties are descriptions of possible customer requirements (e.g. within

an investment advisory process the question under the assumption that your invest-

ment of 10.000 EUROS decreases in value, at which value would you sell your

investment? is related to the willingness to take risks).

– constraints are restricting the combinations of customer requirements and product

properties, e.g. return rates above 9 percent require the willingness to take risks.

Constraints can be deﬁned on the graphical level as well as on the textual level.

In order to support the analysis of advisors, Koba4MS Designer provides a statistical

analysis component which operates on interaction logs of advisory sessions conducted

by online customers or sales representatives.

Process Designer. A recommender process represents possible navigation paths which

deﬁne the way the system adapts its dialog style to the knowledge level and interests

of the customer. Such process deﬁnitions are based on a predicate augmented ﬁnite

state recognizer (PFSR) [17] (constraints describe transitions between different states

of a recommender process) which represents allowed navigation paths within an ad-

visor (see Figure 2). Transition conditions between states of a recommender process

are evaluated using the Koba4MS constraint engine. Based on a layout template deﬁn-

ition, knowledge bases and process deﬁnitions can be automatically (no programming

is needed) translated into an executable advisor (see e.g. Figure 3), where each state of

the process deﬁnition corresponds to a Web-page in the generated application.

Testing & Debugging Knowledge Bases. The increasing size and complexity of recom-

mender knowledge bases makes testing a critical task [18] in the context of successfully

deploying and maintaining recommender applications. Process deﬁnitions (see e.g. Fig-

ure 2) are the basis for automatically generating test cases. Solutions (results calculated

167

by the knowledge base) for generated test cases are presented to the domain expert who

decides on their validity (Result Validation). Test cases deemed as correct by the do-

main expert are used for regression tests. Test case generation in Koba4MS follows a

path-oriented approach (the test cases for each path are derived from the set of solu-

tions to a corresponding constraint satisfaction problem) which allows a high degree of

coverage [19]. The disposable time for testing is restricted, consequently mechanisms

are provided which reduce the amount of tests without reducing the coverage of the

overall test suite (except for random selections). Typically, domain experts agree with

accepting efforts related to testing since solution quality is of serious concern. We can

calculate a complete set of test cases which includes all possible transitions of a process

deﬁnition, but this is only feasible for small and strongly constrained recommendation

tasks. Approaches reducing the number of test cases are presented in [20].

Fig.2. Deﬁnition of customer properties, constraints, recommender processes.

2.3 Runtime Environment

Koba4MS Server. The calculation of solutions for a recommendation task is based

on constraint satisfaction problem solving [21]. Customer properties as well as product

properties are represented as constraint variables. A solution for a given recommenda-

tion task (constraint satisfaction problem) is found if all constraints are satisﬁed. For an

example screenshot of an interface see Figure 3. Note that Koba4MS allows the logging

of advisory sessions which supports the improvement/ﬁne-tuning of the actual know-

ledge base. Interaction logs can be analysed using the statistical analysis component

integrated in Koba4MS Designer.

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3 Used Technologies

Compared to Knowledge-based Recommender applications [1,2], Collaborative Filter-

ing [4,7] and Content-based Filtering [3,5] do not exploit deep knowledge about the

domain in order to determine solutions ﬁtting to the wishes and needs of the customer.

Using knowledge-based approaches, the relationship between customer requirements

and ﬁnancial services can be explicitly modelled in an underlying knowledge base [11].

Such model-based representations are the precondition for applying diagnosis and test-

ing techniques.

3.1 Constraint Satisfaction

Search for Solutions. As already mentioned, Koba4MS problem solving is based

on constraint satisfaction problem solving. A Constraint Satisfaction Problem (CSP)

(C,V ,D) [21] is deﬁned by a set V of variables x

, a set C of constraints c

and a

set D of domains d

which deﬁnes for each variable the set of possible values. A CSP

is solved if there exists a set of instantiations of the variables x

, x

, ..., x

s.t. all

constraints contained in C are satisﬁed. If no solution can be found by the search en-

gine, constraints are relaxed starting with constraints with lowest priority. If nothing but

non-relaxable constraints remain and no solution was found, a repair mechanism is acti-

vated. Solutions are presented in order of their usability for the customer (see Section 3,

Utility of Solutions)

. In addition to constraints, Koba4MS supports tips, i.e. constraints

representing e.g. cross-selling opportunities which are presented to the customer with-

out interrupting the recommender process. Cross-selling conditions are integrated in the

recommender knowledge as well represented as special types of constraints. A tip is:

long-term investments reduce risks, i.e. allow higher return rates than short-term invest-

ments without taking high risks. An example for a tip representing a cross-selling rule

is a married sole wage earner with two children taking out a loan, is also a candidate

for a risk insurance.

Diagnosis and Repair of Requirements. If the result set is empty, conventional recom-

menders tell the user (customer) that no solution was found, i.e. no clear explanation

for the reasons for such a situation is given. Koba4MS supports the calculation of repair

actions for customer requirements (a minimal set of changes allowing the calculation

of a solution). If Σ = {x

= a

, x

= a

, ..., x

= a

} is a set of customer require-

ments (Σ ∪ C has no solution), a repair is a minimal set of changes to Σ (resulting in

Σ’) s.t. Σ’ ∪ C has a solution. The computation of repair actions [13] is based on the

Hitting Set algorithm [14] which exploits minimal conﬂict sets (minimal sets Π ⊆ Σ

of variable instantiations triggering an inconsistency with C) provided by the constraint

solver in order to determine minimal diagnoses and corresponding repair actions.

Test Case Generation. Automated test case generation (for more details see Section

2.2) is based on the deﬁnition of a constraint satisfaction problem. For this purpose a

(complete) set of possible paths through a recommender process is determined. For each

Critiquing of solutions [8] is within the scope of future versions of Koba4MS.

169

path a corresponding CSP is generated and executed - identiﬁed solutions represent test

cases, i.e. possible settings of customer requirements.

3.2 Personalization Concepts

Handling of Proﬁles. Koba4MS includes mechanisms allowing the adaptation of the di-

alog style to the user’s skills and needs [1]. The user interface relies on the management

of a user model that describes capabilities and preferences of individual customers.

Some of these properties are directly provided by the user (e.g. name or personal goals,

or self-estimates such as knowledge about ﬁnancial services), other properties are de-

rived using personalization rules and scoring mechanisms which relate user answers to

abstract dimensions [1] such as preparedness to take risks or interest in high proﬁts (di-

mensions describing the users interests) and knowledge about funds, etc. (dimensions

describing the users knowledge about the domain). Values of the customer proﬁle are

collected from recommender sessions, missing values are asked the customer if they are

needed in the context of a certain recommendation process (e.g. if the customer has not

speciﬁed his/her age up to now then the related question is posed to the customer in the

advisory dialog).

Dialog Style. Customers have different approaches to specify their requirements rang-

ing from the direct speciﬁcation of product parameters (e.g. a certain savings account

running for 3 years) to a general speciﬁcation of their personal goals (e.g. ﬁnancing their

children’s education). An adaptation of the interaction style can signiﬁcantly contribute

to an improved approximation to the behavior of a human sales expert (an experienced

sales assistant adapts his dialog style to the skill level and interests of customers). De-

pending on answers already provided by a customer, the dialog style can be personal-

ized as follows.

– Alternative formulation of questions, e.g. questions posed to expert users can be

differentiated from those posed to customers with less knowledge about the product

domain.

– Rule-based formulation of default-answers, e.g. if the goal of the customer is to put

money by for a rainy day the default answer to a question related to the maximum

accepted decrease in value of the investment is no value decrease accepted.

– Alternative explanations for constraint violations, e.g. if the customer is a novice, a

very general explanation about changes in the pension law is given, more detailed

information can be included for experts.

Utility of Repair Proposals. If no solution can be found for a given set of customer re-

quirements (although all relaxable constraints have been relaxed), Koba4MS provides a

set of possible (minimal) repair actions which allow the calculation of a solution. Differ-

ent customer properties have an assigned priority which indicates the importance of the

variable for the customer. The lower the priority of the variable the higher the probabil-

ity is that the variable is considered as focus of repair actions, e.g. if the type of returns

on investment (at the end of the investment period, dividend payout) is not important

170

<< Back Next >>Restart

Accept

customer

properties

possible

answers

explanations for

questions

control 

elements

provisional

results

<< Back Restart

repair

proposals

Fig.3. Example user interface.

for a customer, this property is primarily considered as a potential candidate for repair

actions. More formally, the personalization of repair proposals is based on the formula

f(x

, x

, ..., x

) =

j=1

p(x

), where f(x

, x

, ..., x

) represents the utility of repair

actions related to the variables x

, x

, ..., x

and p(x

) denotes the customer-speciﬁc

priority of variable x

. Customer-speciﬁc priorities can be either deﬁned statically or

by a customer within the scope of an advisory session.

Utility of Solutions. A solution for a recommendation task is a set (portfolio) of ﬁnan-

cial services. The order of solutions should strictly correspond to the degree a solution

contributes to the wishes of a customer. Koba4MS supports multi-attribute object rat-

ing [1], where each solution entry is evaluated w.r.t. to a predeﬁned set of dimensions.

Proﬁt, availability and risk are examples for such abstract dimensions. Depending on

the weighting of the dimensions for a speciﬁc customer (e.g. a customer is strongly in-

terested in products with high return rates, i.e. compared to availability and risk, proﬁt

is a very important dimension) the set of solutions is ordered using the formula g(x) =

i=1

(x), where n denotes the number of dimensions, g(x) represents the utility

of a solution x, e

represents the interest of the customer in dimension i, and s

is the

contribution of solution x to dimension i.

Presentation of Solutions. For each solution a set of immediate explanations [16] is cal-

culated, i.e. a set of explanations which are derived from variable assignments directly

dependent on selections already made during search. Furthermore, solution-speciﬁc ex-

planations are supported, e.g. if the customer is strongly interested in high return rates

and a solution shows a remarkable return rate, this fact is explicitly mentioned when the

solution is presented to the customer. In contrast to immediate explanations (derived in

the search process), solution-speciﬁc explanations are related to explicitly deﬁned ex-

planation constraints.

171

3.3 Knowledge Acquisition

Knowledge Base Debugging. Effective debugging support for the implementation of

recommender knowledge bases is a critical issue for a successful development and

maintenance of advisors. In Koba4MS we have implemented model-based diagnosis

algorithms [13,14] supporting the identiﬁcation of minimal sources of inconsistencies

in recommender knowledge bases. Similar to the diagnosis and repair of customer re-

quirements, we apply model-based diagnosis techniques in order to identify a minimal

set of constraints ∈ C which - when deleted from the knowledge base - allow consis-

tency restoration.

4 Experiences from Projects

A graphical development environment guaranteeing the maintainability of applications

is a major prerequisite for successfully implementing a knowledge-based advisor. In

the ﬁnancial services domain the implementation and maintenance of knowledge bases

must be supported for non-programmers, i.e. the knowledge acquisition component

must provide intuitive modelling concepts. The overall efforts for implementing an ad-

visor application are between one man month and about a man year where the most

inﬂuencing factors are the complexity of the knowledge bases, the modeling knowledge

of people engaged in the project and efforts related to the implementation of interfaces

to remote systems (e.g. ERP or CRM systems). The correctness of solutions plays a

vital role for the acceptance of the system by sales representatives applying the system

while communicating with the customer. Domain experts do not use formal knowledge

representation formalisms on a daily basis, i.e. effective test and debugging support is

extremely useful and signiﬁcantly improves the effectiveness of the overall advisor de-

velopment process. Experiences from projects indicate a reduction of efforts related to

knowledge base development of about 30-50 percent. The following conclusions can be

drawn from the actual projects based on Koba4MS technologies.

– Knowledge Acquisition. Experiences from projects

show that graphical know-

ledge acquisition is a major precondition for enabling the design and maintenance

of recommender knowledge bases and signiﬁcantly reduces the knowledge acqui-

sition bottleneck between domain experts and knowledge engineers. The usability

has been shown, e.g., in our projects in the ﬁnancial services domain, where domain

experts autonomously develop and maintain the recommender knowledge bases af-

ter a ﬁrst project where they were accompanied by a technical expert.

– Cross Selling. Koba4MS indicates cross-selling opportunities with a corresponding

set of explanations as to why a solution is useful for the customer. The analysis of

sales records e.g. in the digital camera domain shows signiﬁcant improvements in

the sales of add-on and niche products which were neglected previously.

– Routine advisory tasks. Effort reductions related to routine advisory tasks are re-

ported, e.g. ﬁnancial services advisory provided on the homepage relieves sales

representatives from routine advisory jobs.

See e.g. www.hypo-alpe-adria.at (investment advisor) or www.geizhals.at (digital camera ad-

visor deployed on the largest Austrian online product platform).

172

– Documentation. Added value is provided by explanations for calculated service

portfolios which are used as starting point for future advisory sessions. Further-

more, legal regulations can force companies to provide intelligent reporting for the

customer, e.g. due to regulations of the European Union, ﬁnancial service providers

are forced to improve the documentation of advisory sessions - intelligent reporting

is required which includes explanations as to why certain products were offered to

the customer.

– Koba4MS knowledge bases are developed and tested by marketing and sales ex-

perts. Sales representatives can rely on the solutions calculated by the ﬁnancial

advisor and can provide qualiﬁed explanations.

– A set of applications has been implemented on the basis of the recommender tech-

nologies presented in this paper, e.g. the digital camera advisor PIXLA which was

implemented for the largest Austrian online product platform (www.geizhals.at).

This application exhibits about 10.000 successful advisory sessions per month.

Users of www.geizhals.at were interviewed before and after the introduction of

PIXLA. The major result of the study was a statistically signiﬁcant increase of cus-

tomer satisfaction (related to dimensions such as easiness to ﬁnd products etc.).

5 Conclusions

In this paper we have presented the Koba4MS toolsuite which supports the implemen-

tation of knowledge-based recommender applications (advisors). The toolsuite is based

on innovative AI technologies (model-based diagnosis, personalization, constraint sat-

isfaction) which provide an intuitive access to complex products for customers as well

as for sales representatives. Koba4MS includes a graphical development, test and de-

bugging environment which allows the development and maintenance of recommender

knowledge bases for non-programmers. The applicability of the presented concepts has

been shown within the context of commercial projects. Next steps include the integra-

tion of critiquing mechansims into the presentation of results and the application of

model-based diagnosis concepts to the identiﬁcation of inconsistent transitions in rec-

ommender process deﬁnitions.

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