The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
mTrust: discerning multi-faceted trust in a connected world
Abstract
Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.
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(1999) · Cited by 12,854
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 8,959
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
Developing and validating trust measures for e-commerce: an integrative typology
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002) · Information Systems Research · doi:10.1287/isre.13.3.334.81 · Cited by 5,550
A Multilinear Singular Value Decomposition
De Lathauwer, L., De Moor, B., & Vandewalle, J. (2000) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/s0895479896305696 · Cited by 4,106
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401944 · Cited by 3,852
TwitterRank: finding topic-sensitive influential twitterers
Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718520 · Cited by 1,742
Mining knowledge-sharing sites for viral marketing
Richardson, M., & Domingos, P. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775057 · Cited by 1,612
Predicting positive and negative links in online social networks
Leskovec, J., Huttenlocher, D., & Kleinberg, J. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772756 · Cited by 1,470
Propagation of trust and distrust
Guha, R., Kumar, R., Raghavan, P., & Tomkins, A. (2004) · Proceedings of the 13th international conference on World Wide Web · doi:10.1145/988672.988727 · Cited by 1,464
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A Deep Graph Neural Network-Based Mechanism for Social Recommendations
Guo, Z., & Wang, H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2986316 · Cited by 269
Is distrust the negation of trust?: the value of distrust in social media
Tang, J., Hu, X., & Liu, H. (2014) · Proceedings of the 25th ACM conference on Hypertext and social media · doi:10.1145/2631775.2631793 · Cited by 91
Self-citationPopularity Bias in Dynamic Recommendation
Zhu, Z., He, Y., Zhao, X., & Caverlee, J. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467376 · Cited by 82
User Behavior Simulation with Large Language Model-based Agents
Wang, L., Zhang, J., Yang, H., Chen, Z.-Y., Tang, J., Zhang, Z., Chen, X., Lin, Y., Sun, H., Song, R., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3708985 · Cited by 59
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Metadata
- Authors
- Tang, J., Gao, H., & Liu, H.
- Venue
- Proceedings of the fifth ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2012-02-08
- Pages
- 93-102
- OpenAlex
- W2009205701
- Cited by
- 337
- References
- 26
- FWCI
- 18.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tang, J., Gao, H., & Liu, H. (2012). mTrust: discerning multi-faceted trust in a connected world. Proceedings of the fifth ACM international conference on Web search and data mining. https://doi.org/10.1145/2124295.2124309
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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