A Personalized Trust-based Approach for Service Selection in Internetwares Pan Jing1,2, Xu Feng1,2, Xin Xianlong1,2, Lü Jian1,2

1Key Laboratory for Novel Software Technology, Nanjing 210093, China 2Institute of Computer Software, Nanjing University, Nanjing 210093, China

E-mail:{panjing, xf, xxl, lj}@ics.nju.edu.cn

Abstract Internetware is an abstract of the distributed software

system in the open and dynamic Internet which is generally integrated by large numbers of autonomous and heterogeneous software services of various functions. The reliability of the service which acts as a component in Internetware inevitably has an effect on the collaboration in the system and users’ satisfaction on the whole system. So picking the right service from the open environment is a critical problem for developers to build high confidence application systems. However, traditional test measures cannot work when facing with dynamically evolutive characters and separated interests between developers of systems and third-party services in the Internetware. Trust and reputation mechanism is a complementary approach which relies on analyzing collected recommendations from past users to evaluate the software. In this paper, we propose a personalized trust-based approach to do service selection automatically for users. It mainly deals with two problems: 1) trustworthiness: as lacking of a global monitor to record runtime performances of services, we design a recommendation collection method based on the user’s social network; 2) relevance: since the formation of ratings has an impact on the recommendation, we establish a filtering process to weight information according to recommenders’ expectation and capability. 1. Introduction

Internetware is envisioned as a general software paradigm for the application style of resources integration and sharing in the open and dynamic platforms such as Internet[1]. Resources which act as components and play different roles in the system are usually chosen from encapsulated and published services, e.g. web service, a specific kind of service that is identified by a unique URI and its description, discovery and access comply with open Internet standards[2]. In order to maintain a high confidence, Internetware makes great effort to select reliable services to collaborate with. However, traditional reliability measures such as tests[14] cannot work sometimes because 1) the development mode of Internetware is different from the traditional style that software system is under the control of the developer. Since interests between developers of the system and services are separated, implement details of third-party services are hidden from users which makes them wonder the authenticity of tests results offered by service developers; 2) Intenetware is open to numerous users with

changeable requirements. When the operational resultcannot fulfill requirements, self-adaptation mechanismssupport the system to do offline adjustment and online evolution. But the online evolution cannot afford sufficient time to perform through tests before selecting a substitute service. Therefore, we need an auxiliary approach to evaluate services, not only measuring QoS[3] but also some domain-specific properties. Trust and reputation mechanism[5] are widely used to review a resource in the open environment by aggregating subjective opinions resulted from historical interactions. It is quite applicable to service selection in Internetwares as most people use the system to handle daily business and require it to be sufficient to fulfill with personal expectations rather than absolutely correct.

A large number of trust and reputation approaches[4] are proposed to compute aggregated ratings for services of different attributes. However, most approaches are insufficient to be employed to service selection in Internetwares, e.g. [6] allows the user to reason the best service from reported experiences on some publicly accessible forums. But it costs to build an authority center to store all feedbacks and guarantee their correctness since advertisements and imputations in recommendations will definitely influence users’ decisions. [7] lets users make decisions based on combination of their local trust values and global reputation on services. But a mere rating cannot express the rationale of this value explicitly which will reduce the persuasion of recommendations.

In this paper, we propose a personalized trust-based approach that assists consumers to select the right web service according to their own expectations by gathering trustworthy recommendations provided by past users and distinguishing their relevancy to the target. We firstly build an ontology model to describe relations between resources and individuals in the selection environment, e.g. web service. According to the friends list maintained by the user, we collect usage feedbacks as recommendations from distributed sites. Then, by comparing recommenders’ expectations and professional capability, we estimate the service’s satisfaction from the user’s point and finally the user is able to choose the most suitable service from candidates with similar functions.

The rest of this paper is organized as follows: we discuss some related work in Section 2. Section 3 defines important terms and Section 4 describes our approaches in detail. In Section 5, we present the case study and finally conclude the paper and discuss the future work.

2008 International Conference on Advanced Computer Theory and Engineering

978-0-7695-3489-3/08 $25.00 © 2008 IEEE

DOI 10.1109/ICACTE.2008.88

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2008 International Conference on Advanced Computer Theory and Engineering

978-0-7695-3489-3/08 $25.00 © 2008 IEEE

DOI 10.1109/ICACTE.2008.88

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2. Related work In this section, we review some representative

approaches for service selection and compare them with our method from three perspectives: content, source and relevancy of recommendations computation.

Fab[8] is a typical recommendation system (RS) designed to help users sift through the enormous amount of information available on the web. It combines two classical kinds of methods: content-based and collaborative into a hybrid step that utilizes advantages of one approach to avoid the other’s certain limitations. Whatever methods RS uses, it suggests there is a centralized repository to record and store all users’ historical activities. Whether to select a service depends on computing similarity between taste of users and recommenders summarized through historical preference. Thus, the most frequent problems in RS are new users and sparse relationships. In contrast, we aim to gather direct information about services and what we concern about is to collect trustworthy recommendations and differentiate their relevancy with the target.

Prior service selection approaches proposed two major ways to acquire feedbacks from past users: publically accessible forums[6],[7] and active monitors[9],[16]. [6] analyzes the quality of web services based on mining users’ experiences reports from forums while [7] stores reputation which reflect how services comply with expectations in a public repository. This way provides us a rich source to get recommendations. On the other hand, in [16], authors employ some agents to observe performance of available web services in the registry and [9] implements some monitors to record runtime behaviors of web services. This is quite a safe way that uses objective information to do evaluation. However, in our approach, we ask credible friends of the user for recommendations so as to filter some potential advertisements or imputations and reduce the cost of implementing a centralized or several distributed monitors. The trustworthiness of recommendations are weighted by trust relation in the user’s social network[17].

Besides making effort to collect trust recommendations, we add another dimension: relevancy of recommendations to the evaluation target to filter helpful information. In [7], a cognitive trust-based approach is introduced to let the user make selection decisions by combination of their trust policy and public reputation on web services. Other ontology–based techniques suggest to add a context concept to describe the formalization of each recommender’s rating which establishes a foundation to talk about large numbers of opinions[10][12]. [10] improves the classical rating-based approach by classifying recommenders with similar requests. [12] proposes a role-based recommendation evaluation model to discuss recognition and impacts of recommenders’ roles in the domain by recommendation receivers. However, most mentioned methods do not define the relevance

computing methods explicitly, we improve them by not only comparing the similarity between bounds and weights set on various attributes of services by users, but also distinguishing different influences of opinions given by recommenders based on relative professional degrees between domains of their major and the current target.

3. Defining trust and recommendation Since different users suppose to focus on distinct

services’ properties, we define a measurement indicator called satisfaction to estimate how services run to the user’s criterion Definition1(Satisfaction). Satisfaction is the rating which indicates the degree that the service can fulfill the user’s expectation. The expectation is expressed by different bounds and weights allocated on several attributes of services. We use A BS �� to represent the satisfaction that individual A has on service B , which is a normalized real value in the interval[0,1] .

We compute satisfactions based on direct interactions or usage opinions provided by trusted recommenders. However, trust[15] is a complex term which hasn’t formed into a consensus in the literature yet. It can be formalized in multiple ways such as recognized honest trait, experts’ position, similar tastes and so forth. This leads to a problem when considering the transitivity of trust, the more subjective the trust means, the more complicated a trust value can make sense. In order to minimize the subjectivity of a rating, we narrow the meaning of trust and remain the other properties to be discussed in the recommendation. Their definitions are as follows.

Definition2(Trust). Trust is regarded as the dependability relationship between individuals which reflects one’s personality trait. It is expressed as

[0,1]A BT �� � and indicates how much the individual Abelieve in what B said.

Definition3(Recommendation). Recommendation is the opinion shared by the third party and denoted as

[0,1]A BR �� � . Whether an individual accepts a recommendation is a matter related to its trustworthiness and relevance to the evaluation target. We use a function f to compute the above two factors: : ( , )f T RLV R� � ,T refers to trustworthiness, and RLV means relevance. We consider two dimensions of RLV : the capability of recommenders and the similarity between recommenders’ and users’ expectations. 4. The personalized trust-based approach

This approach is proposed where users are looking for the most suitable service to meet their non-functional expectations from many candidates of similar functions. We can divide entities in Internetware into two kinds: individuals and resources. Individuals include persons who use, develop, provide the resource and so on. Resources involve multiple kinds of software services on

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Internet. An ontology model in the Fig.1 gives a service context description including entities and relations in Internetware. Ontology is a convenient tool to describe the knowledge in a specific application domain and makes the necessary reasoning available based on concept defined [18].

Fig.1 The ontology model for the service context The service context defines basic elements: person,

expectation, experience and service’s attribute. The relations between them lead the service selection to perform in four steps: 1) we describe users’ expectations explicitly so as to compute the similarity of expectations late. 2) With the relationship depicted between users and friends, we gather feedbacks for evaluation. 3) Information filtering aims to differentiate various effects of recommendations through comparing expectation similarity and professional capability. 4) The approach aggregates recommendations and selects the service with the highest satisfaction from users’ point of view finally. 4.1. Expectation description

As shown in Fig.1, service’s attribute class has some sub-aspect classes and the user customize the expectation from it. We can either use OWL[19] directly to describe the expectation of users.

The expectation extracted from the ontology model can be formalized as follows which facilitate the similarity computation between expectations:

,Expectation Aspect Constraint�� �Where Aspect is the service’s attribute customized by users and Constraint is the lower bound set on the aspect. For each user, since different numbers and kinds of aspects are paid attention to, the content and length of Expectation is distinct from each other. Additionally, each constraint is composed of the value and weight assigned to express the user’s required level:

,Constraint Value Weight�� � , iValue and iWeight are one-to-one correspondence whose forms are denoted as:

1

1 1

( ... ... )( , ... , ... , )

i n

i i n n

Aspect Aspect Aspect AspectConstraint Value Weight Value Weight Value Weight

� � � � �� � � �� �� �� �� �

Here, iValue is the lower bound of iAspect and iWeight

reflects personalized preferences on it, 1

1n

ii

Weight�

�� .

Usually, setting hierarchical weights explicitly for every aspect is a little troublesome for the user since it is

difficult to quantify their preference precisely into a given interval by assigning each weight a real value. On the contrary, asking the user to compare the important degree of every two aspects is much easier. We apply the analytic hierarchical process (AHP)[20] to compute the weight. So, the user only needs to set the relative importance of two weight, then the weight distribution vector of all concerned service’s attributes can be computed according to the AHP method. 4.2. Recommendation collection

We mainly focus on the common facet of the trust concept which reflects the honest character of one’s personality trait. Namely, if one trusts a friend, he can believe what the friend said is exactly according to the fact. By the formalism of trust, the assertion “Peggy trust the information provided by Tony” is expressed by notation Peggy TonyT �� . It satisfies properties of 1) reflexivity: the individual trust his own information; 2) anti-symmetry: trust relation has direction, namely the individual 1P trust what another one 2P said but contrarily it is not true if there is no such relation defined previously; 3) transitivity: an individual will trust the friend of his friend although the trust degree will attenuate with the grown of the length of transitive chain.

Via the user’s social network, we can collect some trustworthy feedbacks from distributed sites. There exist two kinds of information: the trust relation among recommenders and users, and feedbacks provided by recommenders. Fig.2 shows a discovery process with a limited length and depth whose form is similar with trust credential chain discovery in the trust management[13].The user Sub initiates a discovery target of Sub ObjR �� to find opinions on the service Obj and the same time ask his friends for trust relation with other potential recommenders.

:Use ObjSub:Use ObjD

:Use ObjF:Use ObjC

:Use ObjE:Use ObjB

:User ObjH:Dev ObjG

Sub ObjR ��

Sub BT ��

Sub ET ��

B CT � � C DT ��

C FT ��

D GT ��

F HT ��

Fig.2 Recommendation discovery chain based on trust Expressions 1)~7) shows that there exist several ways

for the subject to obtain runtime behaviours of the object. The real line arrow denotes that the entity on the left side has the friend on the right side while the broken line arrow means that the entity on the left side has interaction records to prove the reliability of the object. All recommendations collected are categorized according to their operational time, and the more close the time is to the present, the more significant the recommendation is. However, different recommenders have recommendations

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of various influences because of their professional capability. We denote it as: :Use ObjSub , the role is in front of the object to show the identity of the subject. Additionally, we use an intelligent tool: mobile agent[11] to collect the recommendations independently on the distributed node of the Internet, filter the information with the computation rule autonomously and then send back the result to aggregate the evaluation value. 4.3. Information filtering

The approach weights collected recommendations according to the expectations’ similarity and professional capability of recommenders.

The first step is computing the similarity of expectations. The computing function � is formed as:

1 2: ( , )Expectation Expectation Sim� � �Where { :Sim s s� is a real value in [0,1]} . Assuming that:

1 1 11 2 12 11 11 12 12, * , *Expectation AC A C Value Weight Value Weight�� ��� �

Analogously, 2 1 21 2 22( , )Expectation A C A C� . i kiAC is the abbreviation for iAspect and kiConstraint in the

kExpectation . The aspect with the same suffix in two expectations should correspond with each other. If one aspect is only concerned by the user, the recommender should set the corresponding term i kiAC equal to 0 in his expectation, and vice versa. We compute the similarity of two expectations by measure the cosine of two vectors’

angle: 1 11 1 21 2 12 2 22

2 2 2 21 11 2 12 1 21 2 22

* *

*

A C A C A C A C

A C A C A C A C�

�

.

After that, we weight recommendations by differentiating the professional capability of each recommender in the service’s domain:

2 1 2 1( ) ( , )* ( )Cap Domain Domain Domain Cap Domain��Here, [0,1]Cap� , and 1( )Cap Domain refers to the recommender’s capability in the domain where he is good at. We assume that one’s professional capability value is known as a recognized reputation by public, e.g. in the domain of online game software, an expert specialized in developing and an ordinary player are assigned different capability ranks. This value means the possibility that the recommender can give the comprehensive and accurate recommendations. � is the function used to compute the similarity of two domains based on their semantic distance. However, to apply this function, it is necessary to have the knowledge of domain and the form of this concept model will inevitably have effect on the result of function � . Without losing generality, we use a simple method to compute � . Assuming that there exists a taxonomy tree which has n layers, the root is on the layer n while the farthest leaf is on the layer 1, the domain that the recommender is good at is on the layer g , and the field that the current service belongs to is on the layer k .The closest common parent node of these two layers is on

the floor denoted as cp . With the common sense that the further the layer is away from the root of the tree, the more similar the siblings are, we suggest each moving step from the first layer to its closest parent node costs 1, that from the second layer to its closest parent node costs 2 and the rest can be computed by analogy. So, the

distance of two domains is:1 1

1 2( , )cp cp

i g j kDis Domain Domain i j

� �

� �

� � � .

It counts total steps from one domain to another along the path in the taxonomy tree. Finally, we get the function � :

1 21 ( , ) /Dis Domain Domain MaxDis� � � , MaxDis is the distance between two farthest leaves in the tree which are not siblings of the same parent node. Moreover, we limit that each recommender has only a role in a domain. If the recommender is a versatile person, we choose the role in a domain which is the most similar with that of the service.4.4 Service selection

This step aggregates all collected recommendations, computes the satisfaction for every candidate service and then select the one with the highest value for the required user. The function f in the Section 3 can be implemented now as: * *f T Cap Sim� , and the overall satisfaction of the user A on the service B is aggregated by recommendations from n recommenders:

,

1

1

* * *

*

nA i A i i i B

A B ni

A j jj

T Sim Cap SS

T Cap

�� ����

���

�

� ��

.

After the user interacts with the selected service, he adjusts the social network according to the performance. We will discuss the update strategy in the further research. 5. Case study

In this section, we use a service selection application as the illustration to show calculation steps:

Peggy intends to develop a daily assistant system in the open environment which provides some basic requirements in everyday affairs such as weather forecast, telephone number query and so forth. Now Peggy wants to integrate another function news report into the system. Peggy concerns about the response time and availability and the latter one is more important than the former one. But she has few experiences in using it directly. Fortunately, she has some friends who are familiar with this kind of service, she decides to describe her expectation on the software and ask friends for recommendations on selecting the right service from many candidate services on the Internet.

We depict this application scene in Fig.3according to the service context model defined in Fig.1. Here, we only list a past user named Tony who used to be a developer in the news report service domain. Other recommendations can be collected by recommendation chain discovery and computed following steps described in the above sections.

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Fig.3 The application scene in the case studyWe compute the satisfaction of two services with two

recommenders each as an illustration. According to the same runtime records, two recommenders and the current user calculate own satisfaction respectively. We assume every two recommenders have the same expectation with each other, but different from that of the user. However they have various capabilities to offer recommendations. It shows in the following Fig.4 that since the loose demand of the recommender is possible to make the satisfaction high than the strict one although the current user has the opposite opinion. If we use the recommendation directly without considering their similarity and capability, we will probably make the incorrect selection decision.

Context Users

Response Availability Cap Sim S T R Value Weight Value Weight

Service1 Past1 1 0.4 0.9 0.6 0.9 0.98 0.8 1 0.67Past2 1 0.4 0.9 0.6 0.6 0.5

Current 1 0.3 0.9 0.7 0.81 Service2

Past3 2 0.5 0.8 0.5 0.9 0.73 0.9 1 0.57Past4 2 0.5 0.8 0.5 0.6 0.6 Current 1 0.3 0.9 0.7 0.77

Fig.4 The service selection example in the case study However, we can find some disadvantages of our

approach as well. The effect of recommendations depends on the numbers and experiences of the user’s friends. The more similar and professional recommenders are, the more accurate their opinions are. So as to handle the new user issues, we had better combine the trust-based approach with other techniques based on reputation mechanism[21]. Moreover, the similarity measure we suggest works based on reducing the influence of the recommendation. In the condition that the recommender’s expectation is stricter than the user, the measure cannot work well. We need to do more simulation and experiment to discuss the applicable environment of our approach and improve its limits.

6. Conclusion In conclusion, our approach has two major

contributions: 1) collecting the trustworthy recommendations: we gather recommendations from reliable friends instead of publicly accessible forum for the user to evaluate the service. This helps to decrease the disturbance of some advertisements and imputations which probably widely exist in the open environment; 2) analyzing the relevance of recommendations from two dimensions: we filter the collected information according to the similarity of expectations and the capability of recommenders. This step reduces weights of some recommendations which will possibly mislead the user to

make decision. However, more experiments and discussions are needed to improve the approach’s performance, e.g. new customer problems. We will investigate on them in the further research.

7. Acknowledgement The research is sponsored by 973 of China

(2002CB312002), 863 Program of China (2007AA01Z178, 2007AA01Z140, 2006AA01Z159), NSFC (60736015, 60721002, 60603034, 60403014) and NSFJ (BK2006712).

8. References [1]Jian Lü, Xiaoxing Ma, XianPing Tao, Chun Cao, Yu Huang, Ping Yu: On environment-driven software model for Internetware. Science in China Series F: Information Sciences 51(6): 683-721 (2008) [2] Ali Arsanjani, Brent Hailpern, Joanne Martin, Peri L. Tarr: Web Services: Promises and Compromises. ACM Queue 1(1): (2003) [3] K.-C. Lee et al., “QoS for Web Services: Requirements and Possible Approaches,” World Wide Web Consortium (W3C) note, Nov. 2003; Available online at: www.w3c.or.kr/kr-office/TR/2003/ws-qos/ [4] Yao Wang, Julita Vassileva: A Review on Trust and Reputation for Web Service Selection. ICDCS Workshops 2007: 25 [5] A. Jøsang, R. Ismail, and C. Boyd. A Survey of Trust and Reputation Systems for Online Service Provision. Decision Support Systems, 43(2), pages 618-644, March 2007 [6] Julian Day, Ralph Deters: Selecting the best web service. CASCON 2004: 293-307 [7] Ali Shaikh Ali, Simone A. Ludwig, Omer F. Rana: A Cognitive Trust-Based Approach forWeb Service Discovery and Selection. ECOWS 2005: 38-49 [8] Marko Balabanovic, Yoav Shoham: Content-Based, Collaborative Recommendation. Commun. ACM 40(3): 66-72 (1997) [9] Le-Hung Vu, Manfred Hauswirth, Karl Aberer: QoS-Based Service Selection and Ranking with Trust and Reputation Management. OTM Conferences (1) 2005: 466-483 [10] Murat Sensoy, Pinar Yolum: Ontology-Based Service Representation and Selection. IEEE Trans. Knowl. Data Eng. 19(8): 1102-1115 (2007) [11] Tesauro G, Chess D M, Walsh W E, et al. A multi-agent systems approach to autonomic computing. In: Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 1. New York: IEEE Computer Society, 2004. 467—471 [12] Yan Wang, Vijay Varadharajan: Role-based Recommendation and Trust Evaluation. CEC/EEE 2007: 278-288 [13] Ninghui Li, William H. Winsborough, John C. Mitchell: Distributed Credential Chain Discovery in Trust Management. Journal of Computer Security 11(1): 35-86 (2003) [14] Antonia Bertolino, Lorenzo Strigini: Using Testability Measures for Dependability Assessment. ICSE 1995: 61-70 [15] Tyrone Grandison, Morris Sloman: A Survey of Trust in Internet Applications. IEEE Communications Surveys and Tutorials 3(4): (2000) [16] S. Ran: A Model for Web Services Discovery with QoS, ACM SIGecom Exchanges, Vol. 4, Issue 1 Spring, pp. 1-10, 2003. [17] Jennifer Golbeck: Generating Predictive Movie Recommendations from Trust in Social Networks. iTrust 2006: 93-104 [18] Seok Won Lee, Robin A. Gandhi: Ontology-based Active Requirements Engineering Framework. APSEC 2005: 481-490 [19] M.Smith, C. Welty, and D. McGuinness, Web Ontology Lanugauge (OWL) Giude, August 2003. [20] Saaty, T.L. The Analytic Hierarchy Process, McGraw-Hill, New York, NY. (1980) [21] Farookh Khadeer Hussain, Elizabeth Chang, Tharam S. Dillon: Defining Reputation in Service Oriented Environment. AICT/ICIW 2006: 177

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