Trust Prediction in Online Communities Employing Neurofuzzy Approach

Figures & data

FIGURE 1 ANFIS Architecture (Jang 1993).

FIGURE 2 Methodology of research.

TABLE 1 Utilized Features for Trust Prediction

ID	Feature	Description
1	Te_numrating_tot	The total number of reviews rated by Trustee
2	Te_numreview_tot	The total number of reviews written by Trustee
3	To_numrating_tot	The total number of reviews rated by Trustor
4	Te_in-degree	The in-degree centrality of Trustee
5	Te_out-degree	The out-degree centrality of Trustee
6	Te_Eigencntr	The eigenvector centrality of Trustee
7	Te_Pagerank	The PageRank centrality of Trustee
8	Te_Rvpr	The Reciprocated vertex pair ratio of Trustee
9	Num_rating _to_te	The number of ratings that were rated by Trustor to Trustee reviews
10	Num_rating _to_te_H	The number of high-value rating that were rated by Trustor to Trustee reviews
11	Num_rating _to_te_L	The number of low-value ratings that were rated by Trustor to Trustee reviews
12	Avg_rating_to__te	The average of rating rated by Trustor to Trustee reviews
13	Jac	The Jaccard similarity coefficient of both Trustor and Trustee adjacents
14	Flag	Target field indicating trust/distrust relationships

FIGURE 3 Confusion matrix (Han, Kamber, and Pei 2011).

FIGURE 4 Schema of holdout method (Han, Kamber, and Pei 2011).

TABLE 2 Statistics of the Employed Dataset

Number of users	˜132,000
Number of trust statements	841,372 statements (717,667 trusts and 123,705 distrusts)
Number of reviews written	1,560,144
Number of rating	13,668,319

TABLE 3 Performance Evaluation of Different Techniques

Technique	AUC	Recall	Precision	F-measure
C4.5	0.907	0.981	0.955	0.968
Logistic regression	0.918	0.9870	0.9356	0.9606
ANN	0.9320	0.9780	0.9487	0.9631
ANFIS	0.9412	0.9845	0.9467	0.9652
Bayesian network	0.9009	0.9232	0.9500	0.9364

FIGURE 5 Comparison of methods in terms of AUC and F-measure.

FIGURE 6 ROC curves for ANFIS and C4.5.

FIGURE 7 ROC curves for ANFIS and ANN.

FIGURE 8 ROC curves for ANFIS and logistic regression.

FIGURE 9 ROC curves for ANFIS and Bayesian network.

Jang, J.-S. R. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23:665–85. doi:10.1109/21.256541.

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Han, J., M. Kamber, and J. Pei. 2011. Data mining: Concepts and techniques. San Francisco, CA: Elsevier Science.

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