# An Extended Framework for Recovering From Trust Breakdowns in Online Community Settings
## 1 Introduction  
The evolution of the Internet has provided a variety of modalities of interaction allowing a diverse range of artificial software agents to cooperate, negotiate, delegate and so on [1]. This new type of online interaction channels contributes to the growth of the e-marketplace, facilitating the exchange of goods between individual parties. In addition, successful e-business transactions crucially depend on establishing, maintaining and managing trust in an online setting [2, 3]. Many computational trust models use reputation information to enhance the level of trust among its members. Reputation information can be used as a guideline for assessing possible interactions, selecting and understanding behaviours of trading partners [4]. Most reputation systems use historical information either reported by each participant's direct observation or indirect feedback from others to derive the reputation of interacting parties [5, 6]. Yet, the fact is that past experience information of each party is sometimes unavailable. For example, when a new agent has been deployed for the first time, historical information about its behaviours apparently does not exist. As a result, it is hard to decide whether the newly deployed agent is fraudulent or trustworthy and it is also difficult to compete with other existing agents. This problem has been recognised as a reputation bootstrapping problem. There are many studies proposing different approaches to provide the solutions to such problems. For example, a mathematical model based on probability theory is proposed to identify the relationship between trust [7], reputation and reciprocity of agents in dyadic interactions. To initialize a trust value of an agent, the notion of a stereotype reputation is introduced basedon the information-sharing among agents [8; 9]. In [10], an initial trust value is calculated from defective transaction rates submitted by consumer agents. If consumer agents for a specific provider agent do not exist or the collected defective transaction rates are insufficient, then default trust is the average value of all existing consumer agents' reputations. A more advanced method is proposed by [PERSON] et al. [11] in which trust mirroring and trust teleportation based on profile similarities are used to assign an agent's initial reputation value. In a service-oriented environment, the study by [PERSON] et al. [12] implements an artificial neural network (ANN) technique to find correlations between features and performance of existing services in order to establish a tentative reputation of an unknown service. Similarly, [PERSON] et al. [13] compute a matching result of the observations sequence between a web service and a user which is modelled as a hidden Markov model and pre-defined trust patterns to bootstrap trust of web services. In a peer-to-peer (P2P) environment, [PERSON] et al. [14] introduce a reputation model based on fuzzy theory to compute a peer trust value in a semantic P2P grid where nodes are grouped according to the semantic similarities between their services. However, when trust is violated as a result of interactions that do not proceed as expected, it gives rise to a question about the possibility that the broken trust may be recovered similarly to trust initialization of new entrant agents [15]. Clearly, recovering trust is more difficult than bootstrapping trust as a violation may lead the trust of a transgressor to plunge below its initial level and lead to a negative state of motivation [16]. As suggested in [17; 18], trust recovery requires a more complex mechanism to explore different factors that cause the decline of trust and can also be used to heal the one's initial negative motivations at the same time. Undoubtedly, some trust violations, for example, intentional fraud or a high severe offences do not deserve to be repaired. Violations caused by an unintentional error or a mistake, on the other hand, should function with a trust recovery mechanism.