Experiments Based on Experience, Recommendation

In this section, the testbed is configured with two types of evidence sources: trustor and recommender. In each of the subsections, the evidence sources are configured differently. The testbed is also configured with static web services. As the experiments are designed to evaluate whether experience and recommendation complement each other in computational trust formation, any observations made concerning static web services should be applicable to the other web service types as well. In terms of web service selection, only the max-trust strategy is evaluated. This is due to the fact that exploration is not needed due to the existence of recommendations.

Computational trust formation is based on applying weighted average to experience-based performance belief and recommendation-based performance belief. Experience-based performance belief is assigned a weight of 0.7. Recommendation-based performance belief is assigned a weight of 0.3. The weight assignment is based on the observation that the trustor would always have its own best interest in mind while that may not be the case with recommenders. If the trustor does not have experiences with a web service, computational trust formation would be based solely on recommendation- based performance belief. In terms of belief formation, experience-based performance belief is based on the averaging of all the trustor’s usage experiences. As for

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recommendation-based belief, evidence source trust formation is based on the averaging of all the calculated recommendation assessments. The experimental results obtained from evidence averaging should be applicable to cases of experience window and weighted average.

9.7.1.1

Trustor and Similar Recommenders

An example of the relationship between and for experience, recommendation and experience and recommendation are shown in Figure 52. An example of the relationship between and for experience, recommendation and experience and recommendation are shown in Figure 53.

In the figures, experience is based on and of Boltzmann in Table 7 of Section 9.5.1.1. This is the best experimental results based on experience. Recommendation is based on Figure 40 and Figure 41 with . The experimental results in the figures demonstrated that in most cases computational trust calculated from experiences and recommendations (similar recommenders) can improve on the experimental results of computational trust calculated from either experiences or recommendations (similar recommenders) in the selection of static web service. The only exception is when in Figure 53. In this case, if the recommender is an oscillating recommender, it could cause the trustor to try out web services with negative usage experiences.

As for the reasoning for the improvement over experience-based computational trust, this is due to the fact that exploration is no longer random but instead is directed by the calculated recommendation-based performance belief. The improvement over recommendation-based computational trust is due to the fact that experience-based performance belief is given more weight and in some cases can help mitigate when recommendation-based performance belief is dominated by misleading recommendations.

Figure 52: Mean Experience (Trustor and Similar Recommenders, Average: , )

Figure 53: Percentage of Positive Experiences (Trustor and Similar Recommenders, Average: , )

9.7.1.2

Trustor and Dissimilar Recommenders

An example of the relationship between and for experience, recommendation and experience and recommendation are shown in Figure 54. In the figure, the experimental results demonstrated that for small number of exploitation, computational trust calculated from experiences and recommendations (dissimilar recommenders) can improve on the experimental results of computational trust calculated

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experience recommendation experience and recommendation

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from either experiences or recommendations (dissimilar recommenders) in the selection of static web service. However, as exploitation increases, recommendation-based performance belief starts to coalesce around a single web service. Since the web service could be the recommendation of dissimilar recommenders, the mean experience ends up being lower than when computational trust is calculated from experiences.

Figure 54: Mean Experience (Trustor and Dissimilar Recommenders, Average: , )

An example of the relationship between and for experience, recommendation and experience and recommendation are shown in Figure 55. In the figure, the experimental results demonstrated that in most cases computational trust calculated from experiences and recommendations (dissimilar recommenders) can improve on the experimental results of computational trust calculated from either experiences or recommendations (dissimilar recommenders) in the selection of static web service. The only exception is when . In this case, if the recommender is an oscillating recommender, it could cause the trustor to try out web services with negative usage experiences. As the recommendations provided by recommenders are usually of the same sign as that of the recommended web service’s usage experience, experience-based computational trust is improved upon due to the trustor seldom having to invoke web service with negative usage experience. As for recommendation-based computational trust, it is improved upon due to the mitigating effect of experience-based

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performance belief on recommendation-based performance belief that is dominated by misleading recommendations.

Figure 55: Percentage of Positive Experiences (Trustor and Dissimilar Recommenders, Average: , )