# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 5 Conclusions  
In this paper, we explored the problem of collecting trust information and exploiting trust's various properties to infer trust in online social networks. Both explicitly presented trust relationships and information inexplicitly embedded in user-generated contents that bear users' attitude, experience, expertise, credibility, trustworthiness, etc., are harvested as  
Figure 8: Performance results of the proposed model and comparison methods evaluated by F\({}_{1}\) score for the second set of experiments. (**a**) Experiment 2s (F\({}_{1}\)). (**b**) Experiment 3s (F\({}_{1}\)). (**c**) Experiment 4s (F\({}_{1}\)). (**d**) Experiment 5s (F\({}_{1}\)). (**e**) Experiment 6s (F\({}_{1}\)). (**f**) Experiment 7s (F\({}_{1}\)). Figure 7: Performance results of the proposed model and comparison methods evaluated by accuracy for the second set of experiments. (**a**) Experiment 2s (Acc). (**b**) Experiment 3s (Acc). (**c**) Experiment 4s (Acc). (**d**) Experiment 5s (Acc). (**e**) Experiment 6s (Acc). (**f**) Experiment 7s (Acc). trust information. A probabilistic graphical model based on a conditional random field for trust inference was proposed, which can effectively take advantage of trust's asymmetric, propagative, non-transitive, and subjective properties. With loopy belief propagation, a message passing algorithm, the model inference was presented and well interpreted. Experiments were conducted to evaluate the proposed model on a real-world online social trust dataset, and the experimental results demonstrated the effectiveness of the proposed model for trust inference. Further improvements to the proposed model can be achieved. Implementing the model inference on cliques rather than pairwise structures may help the model to capture interplay among trust relationships that have same trustors or same trustees more accurately, making it possible to integrate more types of features that convey users' beliefs through complex interactions between users. Our handling of class imbalance for classifications, the underlying fact of which is the sparsity in user relationships in online social networks, is quite simple, and it may be further addressed by introducing penalties. The study presented in this paper is primitive, but the proposed model is promising. Distrust is also a trust relationship type that can be supported by adding an extra label to the trustRelation node in the proposed model. Then, with a proper dataset, a model that can infer relationships of both trust and distrust can be trained. The proposed model also supports quantitative and context-specific trust evaluation, which could be an interesting future study with a proper dataset. By using each user's probability of trusting another user, an individual-oriented personalized trust management system can be built, and many social recommendation tasks will benefit from it. With respect to trust's subjectivity and asymmetric properties, the concept of distinguishing the trustor's attitude, experience and belief from the trustee's expertise, trustworthiness, and credibility when forming a trust relationship--which was shown to be helpful for supervised trust inference in this paper--may help to improve unsupervised methods for trust inference. Finally, it will also be crucial to study the model's sensitivity against attacks. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12105186/s1](https://www.mdpi.com/article/10.3390/app12105186/s1)]([https://www.mdpi.com/article/10.3390/app12105186/s1](https://www.mdpi.com/article/10.3390/app12105186/s1)). Detailed feature statistics used in this paper and full experimental results, including performances evaluated by accuracy, precision, recall and F1 score for each experiment conducted in this paper, are enclosed in Supplementary Materials. Conceptualization, Y.L. and B.W. ; data curation, Y.L. ; methodology, Y.L. ; software, Y.L. ; validation, Y.L. ; writing, Y.L. ; funding acquisition, B.W.