On evaluating the effectiveness of rating similarity-based trust

Chakraverty, Shampa; Yadav, Anil Kumar; Sibal, Ritu

doi:10.1007/s13278-016-0395-0

Cited by 3 publications

(8 citation statements)

References 18 publications

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“…Their experimental results is somewhat contradict those of Tang et al [25]. Chakraverty et al's study focused on the implicit similarity and co-rated itemcount thresholds, finding low precision, recall and coverage for the similarity threshold and better precision, recall and coverage for the co-rated item-count threshold [76]. Nunez-Gonzalez et al [77] considered the trust prediction problem as a classification problem.…”

Section: ) Supervised Approachesmentioning

confidence: 90%

“…Zhang et al [75] with the aim of addressing the 'all good reputation' problem, proposed a multidimensional trust prediction approach called CommTrust, which evaluated trust by mining users' feedback comments [75]. Chakraverty et al [76] introduced a logistic regression-based model that focused on the ratings similarity of users to predict their pairwise trust relations. Their experimental results is somewhat contradict those of Tang et al [25].…”

Section: ) Supervised Approachesmentioning

confidence: 99%

“…Supervised/Unsupervised Context-Aware Dynamic Moradi and Ahmadian [88] U N Y Sanadhy and Singh [89] U N Y Raj and Babu [73] U N N Zhao et al [74] S Y N Zhang et al [93] U Y Y Zhang et al [107] S Y N Zhang et al [75] S N N Zhang et al [108] S Y N Liu et al [109] U Y N Zheng et al [112] U Y N Matsutani et al [94] U N N Tang et al [95] U N Y Zhang and Yu [47] U N N Chakraverty et al [76] S N N Sacco and Breslin [56] S N N Huang et al [96] U N N Li and Wang [117] U Y N Fazeli et al [90] U N N Tang et al [92] U N N Moturu and Liu [97] U N N Nunez-Gonzalez et al [77] S N N Yao et al [98] U N N Huang et al [99] U N N Liu et al [37] S N Y Ma et alL [60] S N N Matsuo and Yamamoto [61] S N Y Grana et al [62] S N N Wang et al [63] S N N Bachi et al [66] S Y N Korovaiko and Thomo [68] S N N Borzymek and Sydow [69] S N N Laspez and Maag [70] S Y N Ghafari et al [65] S Y N Zolfaghar and Aghaie [72] S Y Y Tang et al [25] U N N Wang et al [82] U N N Ghafari et al [58] and [4] U Y N Guha et al [84] U N N Golbeck [85] U N N Wang et al [32] U N N Zheng et al …”

Section: Approachmentioning

confidence: 99%

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A Survey on Trust Prediction in Online Social Networks

et al. 2020

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Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

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Section: ) Supervised Approachesmentioning

confidence: 90%

Section: ) Supervised Approachesmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

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A Survey on Trust Prediction in Online Social Networks

et al. 2020

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“…We extend the analysis of Chakraverty et al [3] where they study the direct implicit similarity trust to the indirect implicit similarity trust used by Papagelis et al [7]. We perform analysis of indirect implicit similarity trust.…”

Section: Methodsmentioning

confidence: 98%

“…In addition to the PCC similarity based trust we have also used the mean absolute difference (MAD) based trust metric for our analysis and used that same propagation technique to both the implicit trusts. Chakraverty et al [3] use Mean of Absolute (MAD) similarity among two members u and v is defined by the following equation:…”

Section: Prior Workmentioning

confidence: 99%

Implicit Trust Propagation

Yadav¹,

Chakraverty²,

Sibal³

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

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The concept of trust plays key role in facilitating interactions and transactions in online systems. It is a major tool for handling information overload in online systems and filtering information by incorporating trust in recommender systems. Network of explicitly trusted users is very sparse. Implicit trust relations can be inferred based on user attributes and behavior. New trust relationships among users can be inferred using trust propagation properties which are not connected by explicit trust to solve the data sparsity problem. Here, we evaluate how effective is indirect implicit trust in predicting the explicit trust using different metrics. We show that indirect implicit trust has relatively higher correlation with rating similarity compared to direct implicit trust. We are able to infer more trust relations using indirect implicit trust as seen from higher recall and coverage values but the accuracy in predicting trust is low compared to direct implicit trust.

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