# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 2 Background
### Challenges of Dishonest Recommenders  
* Dishonesty in recommendation is not always characterized by overtly negative behaviour or a generally low level of trustworthiness. Instead, a recommender, or node in this context, may exhibit seemingly normal behaviour in most interactions but engage in deceptive practices by deliberately providing false recommendations about others. This subtlety makes detecting dishonesty challenging because the overall behaviour of the node might not immediately arouse suspicion. For instance, the graph in Figure 2 illustrates the average trust score towards a dishonest recommender who continues to perform attacks as described in Section 2.3, following the model presented in [16]. In cases of trust manipulation, such as bad-mouthing and ballot stuffing, the trust towards the recommender might naively overlook the misbehaviour, resulting in a high trust score over time for the dishonest recommender. On the other hand, a selfish recommender can be detected if the frequency of its recommendation requests and responses significantly deviates from typical communication patterns. If the trust evaluation of the recommender takes into account the frequency of communication, unusual behaviour can be identified. Specifically, a selfish recommender who often refuses to provide recommendations will exhibit a lower frequency of outgoing recommendations compared to the norm. This deviation can be quantified and monitored over time, leading to a decrease in the trust score of the selfish recommender as their non-cooperative behaviour becomes evident.
* In the field of recommender systems, two predominant methods for detecting dishonest recommenders are the Majority Rule-based [17] and the Deviation from Personal Experience-based approaches [15]. While simple and effective in certain contexts, the Majority Rule-based method falls short in scenarios with collaborative attacks, where a dishonest majority can skew the results. It also risks false positives by mislabelling honest recommenders with unique perspectives. On the other hand, the Deviation from Personal Experience-based method, which compares recommendations against individual user experiences following a defined threshold to detect recommendations that deviate from the personal experience and thus is able to isolate unfair negatives and unfair positive recommendations, offers a more personalized assessment. However, it struggles in situations with limited personal experience and is susceptible to the decay of trust over time, especially when lacking direct interactions with certain nodes. Both methods, therefore, while useful in their respective contexts, exhibit distinct limitations that can impact their effectiveness in accurately identifying dishonest recommenders. These two methods have been defined in the literature under what is called endogenous discounting [18]. The inherent fallacy of endogenous discounting highlights the confirmation bias introduced when claims deviating from prior expectations are ignored.  
Figure 2: The average trust scored for dishonest recommenders over time obtained by CATM.