Personalizing the Search for Persons: A Recommender-

based Approach

Tobias Keim

, Jochen Malinowski

, Gregor Heinrich

, and Oliver Wendt

University of Frankfurt, Mertonstr. 17, 60325 Frankfurt/M., Germany

Fraunhofer IGD, Fraunhoferstr. 5, 64283 Darmstadt, Germany

University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany

Abstract. Recommendation systems are widely used on the Internet to assist

customers in finding the products or services that best fit their individual

preferences. While current implementations successfully reduce information

overload by generating personalized suggestions when searching for objects

such as books or movies, recommendation systems so far cannot be found in

another potential field of application: the personalized search for subjects such

as business partners or employees. This is astonishing as (1) the number of

CV-, assessment- and social network-data available on the Internet is growing

and (2) the complexity and scope of selecting the right partner is much higher

than when buying a book. We argue that recommendation systems

personalizing the search for people need to be grounded on two pillars: unary

attributes on the one hand and relational attributes on the other. We present a

framework meeting these requirements together with an outline of a first

prototypical implementation.

1 Introduction

Personalization systems such as recommender engines in recent years attracted the

interest of many researchers and practitioners. Since Resnick and Varian first

established the term “recommender system” in 1997 [26], researchers have been

improving recommendation quality and scalability of such systems by various means.

While some researchers merged content-based with collaborative filtering in order to

overcome sparsity problems and combine the advantages of both approaches [20]

[29], others focused on how to reduce the dimensionality of the user-item-matrix

underlying collaborative filtering approaches [30] [32]. Today, recommendation

systems successfully assist consumers on the Internet in finding products or objects

based on items similar to the ones the customer himself previously liked or based on

items that other customers similar to him liked in the past. However, personalization

systems are not yet applied when searching for people or subjects. Thus our research

question is: What are necessary theoretical enhancements for human recommender

systems? We argue that the various bilateral and relational aspects that need to be

considered when bringing individuals together imply extending existing approaches

Keim T., Malinowski J., Heinrich G. and Wendt O. (2005).

Personalizing the Search for Persons: A Recommender-based Approach.

In Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces, pages 125-134

DOI: 10.5220/0001421801250134

 SciTePress

by relational data. Building on existing theory and own prior research, we derive

concrete requirements and present an outline for a recommendation system

personalizing the search for individuals.

2 Research Motivation

Information technology in recent years has transformed (1) the ways people find work

as well as (2) the ways they effectively work together. With regard to the first aspect,

own longitudinal empirical research with the Top-1.000-companies in Germany as

well as with over 11.000 job seekers shows that the Internet has replaced print media

as the most important recruitment channel [16] [17]

. With 78% of all vacancies being

published within the career section of the corporate website and 49% of open jobs

being posted on Internet job-portals, IT-supported channels dominate print media

(30%) as a way to attract candidates. Also, over the years the ratio of actual hires

generated through job ads on the Internet rises reaching 58% in 2004 [17]. When

considering the later stages of the recruitment process such as the treatment of

incoming applications and the (pre-) selection of candidates, a diminished importance

of IS-support can be observed. However, as digital applications lower application

costs, the number of incoming (electronic) applications increases. Thus, companies

seek adapted IS-support for the selection stage in order to process the masses of

incoming applications efficiently.

85,0%

52,4%

37,4%

30,0%

16,6%

76,8%

48,9%

30,3%

23,1%

12,5%

20%

40%

60%

80%

100%

Corporate Website Job-portal Print media Job Centers Others

2003 2004

52,8%

35,3%

4,3%

7,5%

58,2%

27,0%

5,2%

9,6%

20%

40%

60%

80%

100%

Internet Print media Job Centers Others

2003 2004

Fig. 1. Ratio of vacancies published Fig. 2. Ratio of jobs filled

While this empirical research deals with how people find work, other research strands

are concerned with how information systems change the ways people effectively work

together once the candidate is recruited. Starting from Malone and Laubacher’s vision

of the “e-lance economy” [21], special attention was paid to the ways communication

channels and “discontinuities” of space, time and organizational boundaries

characteristic of virtual work influence collaboration patterns [2] [34]. Thus, as work

Companies selected based on revenues; between 151 and 196 companies responding between

2002 and 2004

126

in changing projects and organizational settings gains importance, individuals are

more frequently matched to new colleagues within their lifetime. Beside this, systems

for ad hoc short-term expert identification streamline the way knowledge is accessed

and exchanged between different projects or units beyond document management [1]

[9].

3 The Personalized Search for Persons

From these considerations that (1) matching situations within a person’s work history

will increase and (2) decision support for the matching of collaboration partners will

emerge, we started to develop a system for the personalized search for individuals. In

the following, we present requirements for such a person-recommender.

3.1 Requirements for Recommending Persons

Team configuration for work contexts has been analyzed by a variety of disciplines.

Typically, such problems are considered under the perspective of task-related and

social aspects [12], human and social capital or person-job fit and person-team or

person-organization fit [31]. Thus, successful team design needs to consider two

dimensions:

• The matching of individuals to tasks for which the candidate possesses the

skills and abilities to carry them out.

• The matching of individuals to other individuals with whom the person is

able to collaborate successfully.

This latter dimension has major implications for the design of a person-recommender

as we cannot consider the selection of candidates as a unilateral decision. While the

customer chooses the movie he wishes to watch and not vice versa, this is not the case

when recommending people. Selecting a candidate or partner is a bilateral selection

decision in which not only the attributes of the item or individual itself need to be

considered, but also the relationship between these items or individuals. In separation

to the former attributes that can be tied directly to the individual, that we refer to as

unary attributes, we denote the latter attributes as relational attributes. Thus, we retain

the following key differences when recommending subjects instead of objects:

• Recommending people is a bilateral process that needs to take into account

the preferences not only of a single person (the active user), but of several

persons.

• Even more, recommendations cannot be based on the attributes tied to the

items or persons in consideration only, but need to incorporate the

underlying relational structure by means of relational attributes.

• Finally, as every individual is considered to be unique, we cannot

recommend a single item or person several times such as in the case of a

127

movie or book. As every person can only be selected once, recommendations

on the item-level are not repeatable. Thus, recommendations cannot be

solely based on a user-item matrix but need to incorporate “content”-

elements such as the unary and relational attributes mentioned above.

3.2 Towards a Person-Recommender

On our way towards a person-recommender, we implemented two complementary

approaches: a CV-recommender and a social network browser that are both going to

be presented briefly hereunder. Afterwards we describe how both approaches can be

combined leading to a relational recommender system.

The CV-Recommender. In a first step, we built a system recommending CVs that

are similar to resumes previously selected by the same recruiter for a specific job-

profile considered. The probabilistic hybrid recommendation engine is based on a

latent aspect model that understands individual preferences as a convex combination

of preference factors [10] [11] [25]. As depicted in figure 3, the recruiter together

with the job description is represented in variable x, the preference factors being

modeled in variable z. In coherence with our prior considerations, the recruiter by

rating a candidate profile or CV with variable v = {"qualified", "not qualified"} does

not rate the person itself, but the sum of its attributes. These “content”-elements,

taken from the candidate's resume are composed of a quadruple such as

a=("mathematical skills", "diploma grade", "1.0", "University of Frankfurt"). Thus,

the rating value v depends indirectly on the position considered x and directly on the

candidate’s attributes a. With a set of observed values v for an attribute assessed by x

and assigned to a, we are able to estimate the model parameters using an Expectation

Maximization (EM) algorithm. A detailed description of the approach together with

validation results can be found in [4].

The Social Network Browser. As the CV-recommender is focused on what we

called unary attributes earlier, we modeled relational attributes in a complementary

approach. The network browser shown in figure 4 visualizes trusted social relations

that the user then can manually browse, filter the network and search for particular

nodes. The social relations used are recommendations between people based on

“historic” experience as well as swift trust assessments from candidate interviews via

video conferences. A more detailed description of the approaches to swift and historic

trust modeled within the system and their elicitation from a user community can be

found in [18] and [9]. When navigating the resulting network, by filtering and

searching techniques it is possible to identify relevant persons in the graph according

to different criteria. This way, important trusted actors in the network can be

identified either from an ego-centered position (of the searcher) or globally using

graph analysis methods such as shortest-path, relative importance and others also used

in social network analysis [33]. In addition, such filtering can initially apply relevant

competence criteria, which creates a trust network contextualized on the queried

competencies. The motivation of this idea is closely related to research on the

relationship between trust, interpersonal cooperation and organizational effectiveness

such as [3] or [14].

128

x Assessor and actual target attributex Assessor and actual target attribute

z Latent influencing factors of attribute

value

z Latent influencing factors of attribute

value

v Assessed target attribute valuev Assessed target attribute value

a Partner attribute a Partner attribute

Fig. 3. The probabilistic CV-Recommender Fig. 4. The network analyzer

4 Towards the Relational Person-Recommender

In order to meet the requirements previously defined, we need to combine the

predictive capabilities of the CV-recommender with the descriptive capabilities of the

network browser in an automatic setting. This is based on our previously defined

requirements where we stated that a person-recommender not only needs to consider

individual but also relational attributes. From a theoretical perspective, this is an

interesting idea as already Granovetter showed that labor market processes are rooted

in social relations [5]. Montgomery argued that the higher quality of information

gained from contact networks reduces frictions when entering a new job [22]. Also,

the reductions of attraction costs [28] and of screening costs have been mentioned as

advantages of partner identification over networks [19].

In order to build such a relational recommender, we developed a trust

computational model. Conforming to Richardson, Agrawal and Domingos (2003), we

assume that trust can be expressed in a singular value even though it is a complex and

multidimensional construct. (In the above network browser, we adopted this scheme

already by aggregating the values of the different trust dimension values.) Our current

research builds on trust propagation as demonstrated in [6]. Based on findings in the

literature and own theoretical considerations we defined three trust propagation and

prediction scenarios as depicted in Figure 5(a)-(c).

Figure 5(a) illustrates how the trust level between individuals A and C can be

inferred given the trust values t

and t

[27]. Figure 5(b) shows a typical

collaborative filtering approach to trust propagation that, based on three given

relations between four people, infers the missing trusted relation [6]. As a

complementary approach to trust propagation, we aim to directly combine individual

and relational attributes as depicted in Figure 5(c). Based on the existing individual

profiles A, B, C and D as well as a single existing trusted relationship t

A,B,

we will

calculate similarities between user pairs. Dependant on these distances d(x,y) as well

as the characteristics of the existing trusted relation t

A,B,

the system will recommend or

129

not a relationship between people unknown so far. We denote this approach as

similarity-based trust propagation. Our next steps include the further development of

our existing integrated prototype and its validation with real-life data. Also, we aim to

add social network data as an additional variable of the model and extend it by

different relation types.

t‘

A B

C D

t‘

A B

C D

t‘

d(A,C) d(B,D)

Fig. 5(a). Direct trust

propagation

Fig. 5(b). Collaborative

trust propagation

Fig. 5(c). Similarity-based

trust propagation

As a basis for the predictive approach, we postulate two work hypotheses: The first

hypothesis is that the unary (i.e., propositional) and relational attribute structure

latently captures personal qualities that generate degrees of trust, possibly conditioned

on specific situations and roles. For instance, looking at a known relational

confidence attribute with a source A and a target B (e.g., A assesses B), it is predicted

that similar relations (with respect to type and weight) can be measured for sources

similar to A and targets similar to B. E.g., if A assesses B positively, C similar to A is

predicted to assess D similar to B positively, as well.

The second hypothesis of the approach is that some dimensions of trust are

transmissible through a referral network. This means, for example, that looking at

such a higher-order trust relation, A trusts B and B trusts C, again possibly

conditioned on a situation or role, trust from A to C can be predicted. This is the

conceptual basis of referral systems, such as ReferralWeb [15] [36]. The question is

what trust dimensions do exhibit this transitive behaviour to which degree.

In particular, the first hypothesis can be mapped to the emergent scientific area of

statistical relational learning (SRL), in which graph properties are learned from data

and the local graph topology surrounding newly observed nodes are predicted. In this

context, we note the work of Jensen, Neville and Wolfe [24] [35] [13] and of

Heckerman, Meek and Koller [8], as a basis for a generic social network prediction

algorithm. The second hypothesis is related to the friend-of-a-friend principle, which

is the basis for transitive trust relations and in fact the basis of the existing system

already.

Further, we plan to connect actors with documents to cluster actors by their

authorship and roles. This extends the idea of explicit profile creation to implicit

methods of profile creation, thus allowing for bootstrapping a real system by

connecting it to existing document bases. A scientific basis for work into this

direction can be found in [23]. Merging both the content and the social network into a

‘smart’ collaboration network to us seems a promising idea when considering the

many real-world knowledge management problems and applications. However,

several challenges appear when modelling profiles for the predictive approach to

partner matching. These are:

130

• the modelling of complementarity and compatibility for team building

scenarios. This includes the incorporation of research on matching different

personal traits with express expertise measures to optimize team staffing.

• the capturing of “inter-rater trust”. Within this functionality, the bias of a

rater will be used to remove bias from ratings and will also be incorporated

as a specific rater characteristic. This has been partly solved in our existing

Opal system via a matrix-based assessment browser as presented in [7].

• the resolution of disreputative scenarios. Situations in which candidates are

assessed badly must be resolved in a way that conserves overall integrity and

privacy in the community but that still allows marking negative experiences.

This is an often-encountered scenario where most rating-based systems

capitulate.

5 Validation approach

In order to validate our approach we currently design an experiment as part of a

student workshop. We plan to test the aspects of the described recommendation

framework in an incremental way. First, students are supposed to enter their CV data

into a web-based prototype. The data capturing hereby follows the same rules as it is

nowadays done in the various existing job-portals. The CV data together with

manually created ratings regarding the match of the students with several job-profiles

is then used as input to train the CV-recommender. Based on this training data the

system should then be able to predict the match between students and job profiles.

In a second step, Students will be asked to enter relational data into the prototype

such as their relations towards fellow students. The relations will be defined based on

its direction, duration and intensity. The captured data should then serve as input for

the trust computational model. Based on this training data the system should be able

to predict previously unknown relations. Finally we aim to combine the separate

results into an integrated approach for personalizing the search for persons.

6 Conclusion

In this paper we argued that recommendation systems so far personalize only the

search for objects, but not for subjects. We showed that theoretical extensions such as

the integration of relational as well as bilateral aspects into current approaches are

necessary in order to build a system personalizing the search for individuals. Based on

these requirements and building up on two implementations from previous research,

we presented an outline of a first existing prototype integrating both approaches into a

single system. Our next steps include the extension of this implementation as well as

its validation with real-life data as part of a student workshop to be carried out. The

objective is to enhance the matching quality of interpersonal partnership especially for

collaboration scenarios by building a bilateral as well as relational recommendation

engine personalizing the search for individuals.

131

Acknowledgements. We gratefully acknowledge the support of the European Union

under the Fifth Framework Programme Information Society Technologies (contract

number: IST-2000-28295).

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