Abstract
There are two key characteristics of users in trust relationships that have been well studied: (1) users trust their friends with different trust strengths and (2) users play multiple roles of trusters and trustees in trust relationships. However, few studies have considered both of these factors. Indeed, it is quite common for someone to respond to his/her friend that they trusted him/her, which indicates that there exist two kinds of information between each pair of users: the trust influence of trustee on truster and the feedback influence of truster on trustee. Considering this problem, we propose a novel adaptive method to learn the trust influence between users with multiple roles of truster and trustee for recommendation. First, we propose to introduce the concept of latent trust strength to learn adaptive role-based trust strength with limited values for each trust relationship between users. Second, because there is only one training example to learn each parameter of latent trust strength, we further propose two regularization methods by building relations between latent trust strength and user preferences to guide the training process of latent trust strength. After that, we develop a new recommendation method, RoleTS, by integrating the role-based trust strength into a previous recommendation model, TrustSVD, which considers both explicit and implicit information of trust and ratings. We also conduct a series of experiments to study the performance of the proposed method. Experimental results on two public real datasets demonstrate that the proposed method performs better than several state-of-the-art algorithms.
Similar content being viewed by others
References
Anandhan A, Shuib L, Ismail MA, Mujtaba G (2018) Social media recommender systems: review and open research issues. IEEE Access 6:15608–15628
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12:331–370
Cai ZQ, Hu H (2018) Session-aware music recommendation via a generative model approach. Soft Comput 22:1023–1031
Calero Valdez A, Ziefle M, Verbert K, Felfernig A, Holzinger A (2016) Recommender systems for health informatics: state-of-the-Art and future perspectives. In: Holzinger A (ed) Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Lecture Notes in Computer Science, Springer International Publishing, pp 391–414
Chaney AJ, Blei DM, Eliassi-Rad T (2015) A probabilistic model for using social networks in personalized item recommendation. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys ’15. ACM, New York, pp 43–50
Chen X, He F, Yu H (2019) A matting method based on full feature coverage. Multimed Tools Appl 78:11173–11201
Chin JY, Zhao K, Joty S, Cong G (2018) ANR: aspect-based neural recommender. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18. ACM, New York, pp 147–156
Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16. ACM, New York, pp 191–198
Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22:143–177
Gunawardana A, Shani G (2009) A survey of accuracy evaluation metrics of recommendation tasks. J Mach Learn Res 10:2935–2962
Guo G, Zhang J, Yorke-smith N (2015) trustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial, Austin, pp 123–129
Guo L, Ma J, Chen Z, Zhong H (2015) Learning to recommend with social contextual information from implicit feedback. Soft Comput 19:1351–1362
Hofmann T, Schölkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann Stat 36:1171–1220
Hou N, Yan X, He F (2019) A survey on partitioning models, solution algorithms and algorithm parallelization for hardware/software co-design. Des Autom Embed Syst 23:57–77
Hu L, Cao J, Xu G, Cao L, Gu Z, Cao W (2014) Deep modeling of group preferences for group-based recommendation. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI’14. AAAI Press, Québec City, pp 1861–1867
Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10. ACM, New York, pp 135–142
Ju C, Wang J, Xu C (2018) A novel application recommendation method combining social relationship and trust relationship for future internet of things Multimedia Tools and Applications
Kieseberg P, Malle B, Frühwirt P, Weippl E, Holzinger A (2016) A tamper-proof audit and control system for the doctor in the loop. Brain Informatics 3:269–279
Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08. ACM, New York, pp 426–434
Koren Y (2010) Collaborative filtering with temporal dynamics. Commun ACM 53:89–97
Leng J, Jiang P (2016) A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowl-Based Syst 100:188–199
Li H, He F, Yan X (2019) IBEA-SVM: an indicator-based evolutionary algorithm based on pre-selection with classification guided by SVM. Applied Mathematics-A Journal of Chinese Universities 34:1–26
Li K, He F, Yu H (2018) Robust visual tracking based on convolutional features with illumination and occlusion handing. J Comput Sci Technol 33:223–236
Li K, He F, Yu H, Chen X (2019) A parallel and robust object tracking approach synthesizing adaptive bayesian learning and improved incremental subspace learning. J Parallel Distrib Comput 13(5):1116–1135
Li K, He F, Yu H, Chen X (2017) A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Applied Mathematics-A Journal of Chinese Universities 32:294–312
Liang Y, He F, Li H (2019) An asymmetric and optimized encryption method to protect the confidentiality of 3D mesh model. Adv Eng Inform 42:100963
Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32
Luo J, He F, Yong J (2019) An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm. Intelligent Data Analysis 23:1291–1308
Lv X, He F, Cai W, Cheng Y (2019) An optimized RGA supporting selective undo for collaborative text editing systems. J Parallel Distrib Comput 132:310–330
Lv X, He F, Cai W, Cheng Y (2018) Supporting selective undo of string-wise operations for collaborative editing systems. Futur Gener Comput Syst 28:41–62
Lv X, He F, Yan X, Wu Y, Cheng Y (2019) Integrating selective undo of feature-based modeling operations for real-time collaborative CAD systems. Futur Gener Comput Syst 100:473–497
Ma H (2014) On measuring social friend interest similarities in recommender systems. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14. ACM, New York, pp 465–474
Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’09. ACM, New York, pp 203–210
Ma H, Yang H, Lyu MR, King I (2008) SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08. ACM, New York, pp 931–940
Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with Social Regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11. ACM, New York, pp 287–296
Ma H, Zhou TC, Lyu MR, King I (2011) Improving recommender systems by incorporating social contextual information. ACM Trans Inf Syst (TOIS) 29:9
Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Platt JC, Koller D, Singer Y, Roweis ST (eds) Advances in Neural Information Processing Systems 20, Curran Associates, Inc, pp 1257–1264
Pálovics R, Benczúr AA, Kocsis L, Kiss T, Frigó E (2014) Exploiting temporal influence in online recommendation. In: Proceedings of the 8th ACM Conference on Recommender Systems, ACM, pp. 273–280
Pan Y, He F, Yu H (2018) An adaptive method to learn directive trust strength for trust-aware recommender systems. In: 2018 IEEE 22Nd International Conference on Computer Supported Cooperative Work in design (CSCWD), pp 10–16
Pan Y, He F, Yu H (2019) A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems. Neurocomputing 332:137–148
Pan Y, He F, Yu H, Li H A correlative denoising autoencoder to model social influence for Top-N Recommender System. Frontiers of Computer Science. https://doi.org/10.1007/s11704-019-8123-3
Rafailidis D, Crestani F (2016) Collaborative ranking with Social Relationships for top-N recommendations. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’16. ACM, New York, pp 785–788
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI ’09. AUAI Press, Arlington, pp 452–461
Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Ricci F, Rokach L, Shapira B (eds) Recommender Systems Handbook. Springer, Boston, pp 1–34
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW ’01. ACM, New York, pp 285–295
Shanmuigapriya T, Swamynathan S (2018) Reliability score inference and recommendation using fuzzy-based technique for social media applications. Soft Comput 22:8289– 8300
Sun J, He F, Chen Y, Chen X (2016) A multiple template approach for robust tracking of fast motion target. Applied Mathematics-A Journal of Chinese Universities 31:177–197
Tang J, Gao H, Hu X, Liu H (2013) Exploiting homophily effect for trust prediction, ACM, New York
Tang J, Gao H, Liu H (2012) mTrust: discerning multi-faceted trust in a connected world. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM ’12. ACM, New York, pp 93–102
Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3:1113–1133
Tarus JK, Niu Z, Kalui D (2018) A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Comput 22:2449–2461
Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 448–456
Wang H, Zhang P, Lu T, Gu H, Gu N (2017) Hybrid recommendation model based on incremental collaborative filtering and content-based algorithms. In: 2017 IEEE 21St International Conference on Computer Supported Cooperative Work in design (CSCWD), pp 337–342
Wang M, Ma J (2016) A novel recommendation approach based on users’ weighted trust relations and the rating similarities. Soft Comput 20:3981–3990
Wu H, Zhang Z, Yue K, Zhang B, He J, Sun L (2018) Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl-Based Syst 145:46–58
Wu Y, He F, Zhang D, Li X (2018) Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans Serv Comput 11:341–353
Xiao Y, Wang G, Hsu CH, Wang H (2018) A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique. Soft Comput 22:6785–6796
Yan X, He F, Hou N, Ai H (2018) An efficient particle swarm optimization for large-scale hardware/software co-design system. International Journal of Cooperative Information Systems 1741001:27
Yang B, Lei Y, Liu J, Li W (2017) Social Collaborative Filtering by trust. IEEE Trans Pattern Anal Mach Intell 39:1633–1647
Yao W, He J, Huang G, Zhang Y (2014) Modeling dual role preferences for trust-aware recommendation. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14. ACM, New York, pp 975–978
Yera R, Martínez L (2017) Fuzzy tools in recommender systems: a survey. International Journal of Computational Intelligence Systems 10:776–803
Yong J, He F, Li H, Zhou W A Novel Bat Algorithm based on Cross Boundary Learning and Uniform Explosion Strategy. Applied Mathematics-A Journal of Chinese Universities. https://doi.org/10.1007/s11766-019-3714-1
Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl 77:24097–24119
Yu H, He F, Pan Y (2019) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl 78:11779–11798
Yu L, Pan R, Li Z (2011) Adaptive social similarities for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, ACM, pp 257–260
Zhang S, He F, Ren W, Yao J Joint learning of image detail and transmission map for single image dehazing. The Visual Computer. https://doi.org/10.1007/s00371-018-1612-9
Zhao T, McAuley J, King I (2014) Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14. ACM, New York, pp 261–270
Zhao WX, Li S, He Y, Wang L, Wen JR, Li X (2016) Exploring demographic information in social media for product recommendation. Knowl Inf Syst 49:61–89
Zhao XW, Guo Y, He Y, Jiang H, Wu Y, Li X (2014) We Know What You Want to buy: a demographic-based system for product recommendation on microblogs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14. ACM, New York, pp 1935–1944
Funding
This study was funded by the National Science Foundation of China (Grant No.61472289) and the National Key Research and Development Project (Grant No.2016YFC0106305).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
Yiteng Pan declares that he has no conflict of interest. Fazhi He declares that he has no conflict of interest. Haiping Yu declares that she has no conflict of interest. Haoran Li declares that he has no conflict of interest.
Additional information
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pan, Y., He, F., Yu, H. et al. Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems. Appl Intell 50, 314–327 (2020). https://doi.org/10.1007/s10489-019-01542-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-019-01542-0