# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 4 Experiments
### Performance Comparisons  
Secondly, we report the comparative performance results achieved by using our model and other methods discussed in the previous section. For these experiments, all available feature datasets and their combinations are free to use for experimenting. The performances for each method evaluated by the accuracy and F\({}_{1}\) score metrics, and experiments in which the best performances were achieved are reported in Tables 9 and 10. Full results, including precision and recall for each experiment, are reported in the Supplementary Materials. As has been already stated, the auxiliary features only fit in the proposed model, and all the other types of features can be used for all methods. Figure 6: Performance results of the proposed model evaluated by Accuracy (Acc) and F\({}_{1}\) score for the first set of experiments. (**a**) Experiments 1, 2, 5, 6, 8 (Acc). (**b**) Experiments 1, 3, 5, 7, 8 (Acc). (**c**) Experiments 1, 4, 6, 7, 8 (Acc). (**d**) Experiments 1, 2, 5, 6, 8 (F\({}_{1}\)). (**e**) Experiments 1, 3, 5, 7, 8 (F\({}_{1}\)). (**f**) Experiments 1, 4, 6, 7, 8 (F\({}_{1}\)). From the experimental results listed in the tables and the Supplementary Materials, the first observation is that our proposed method outperforms other comparison methods in terms of accuracy and F1 score, if all types of features are free to use. The second observation is that UGC features (\(\mathcal{F}_{\text{UGC}}\)) were always involved in the used features when all methods achieved their respective best or second best performances. The fact that there is trust construct embedded in UGC data is proved again, but through the results by comparison methods this time. Consequently, one can harvest a user's trust information from UGC data from the user and his \"friends\" including trustors and trustees, which thus bears witness to Assumption 1 we made in Section 3.1. Last but not least, in contrast to the results achieved by our proposed model, SVM and decision tree generated worse results by adding UGC features than user profile features, and all three comparison methods had improved results by using trust propagation features. One possible reason for the performance divergence's cause is the very large number and dimensions of UGC features employed in the three comparison methods; meanwhile, a few yet powerful trust propagation features greatly improved their performance when working with either UGC features or user profile features. Another reason that our model behaved differently in these experiments is that ours can balance a massive number of features which may counter each other, and can also \"capture and numericalize\" interplayed features. A peek at Recall: higher Precision or higher Recall for trust relationship prediction in OSNs? For a classification task, the outcome for it determines the expectations for the precision and recall in the task's result. For the task of trust relationship prediction, or more generally friend recommendation, in an online social network that exists in an online social service, there are some considerations that might help with making the decision. In OSNs, friend relationship or trust relationship recommendation is an efficient way for users to get started and engaged more in the online social service's activities so that the online social service's revenue could gain. From the user's perspective, the recommendation should provide users with accurate sets of users based on their homophily and existing friend/trust networks, and the expected precision of the recommendation or the prediction is to be high ideally. On the other hand, it is probable that the number of candidates being recommended to a user will be few due to the user's strict criteria, or nearly none, which would eventually make the recommendations less effective; and thus, from the online service's operating and managing perspective, in addition to suggested users who satisfy the user's criteria, it is essential to recommend extra possible candidates to the user, although these candidates