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A comprehensive social matrix factorization for recommendations with prediction and feedback mechanisms by fusing trust relationships and social tags

  • Data analytics and machine learning
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Abstract

Social relationships play an important role in improving the quality of recommender systems (RSs). A large number of experimental results show that social relationship-based recommendation methods alleviate the problems of data sparseness and cold start in RSs to some extent. However, existing recommendation methods have difficulty in accurately obtaining user features and item features, which seriously affects recommendation system performance. To accurately model social relationships and improve recommendation quality, we use both explicit (e.g. user-item ratings, trust relationships) and implicit (e.g. social tags) social relationships to mine users’ potential interest preferences; thus, we propose a social recommendation method incorporating trust relationships and social tags. The method maps user features and item features to a shared feature space using the above social relationship, obtains user similarity and item similarity through potential feature vectors of users and items, and continuously trains them to obtain accurate similarity relationships to improve recommendation performance. The experimental results demonstrate that our proposed approach achieves superior performance over the other social recommendation approaches.

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References

  • Aghdam MH (2019) Context-aware recommender systems using hierarchical hidden Markov model. Physica A 518:89–98

    Article  Google Scholar 

  • Ahmadian S, Afsharchi M, Meghdadi M (2018) An effective social recommendation method based on user reputation model and rating profile enhancement. J Inf Sci 28:180–192. https://doi.org/10.1177/0165551518808191

    Article  Google Scholar 

  • Ahmadian S, Joorabloo N, Jalili M et al (2020) A social recommender system based on reliable implicit relationships. Knowl Based Syst 192:105371. https://doi.org/10.1016/j.knosys.2019.105371

    Article  Google Scholar 

  • Azadjalal MM, Moradi P, Abdollahpouri A et al (2017) A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowl-Based Syst 116:130–143

    Article  Google Scholar 

  • Bagher RC, Hassanpour H, Mashayekhi H (2017) User trends modeling for a content-based recommender system. Expert Syst Appl 87:209–219

    Article  Google Scholar 

  • Can U, Alatas B (2019) A new direction in social network analysis: Online social network analysis problems and applications. Physica A Statal Mech Appl 535(1):1–13

    Google Scholar 

  • Cao Y, Li W, Zheng D (2018) An improved neighborhood-aware unified probabilistic matrix factorization recommendation. Wireless Pers Commun 102(4):3121–3140

    Article  Google Scholar 

  • Chen R, Hua Q, Chang Y et al (2018) A survey of collaborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks. IEEE Access 6(1):64301–64320

    Article  Google Scholar 

  • Cohen D, Aharon M, Koren Y, et al (2017) Expediting exploration by attribute-to-feature mapping for cold-start recommendations. In: Proceedings of the 11th ACM conference on recommender systems (RecSys’17), pp 184–192

  • Feng S, Cao J, Wang J et al (2017) Recommendations based on comprehensively exploiting the latent factors hidden in items’ ratings and content. ACM Trans Knowl Discov Data 11(3):35–46

    Article  Google Scholar 

  • Gao Q, Gao L, Fan J et al (2016) A preference elicitation method based on bipartite graphical correlation and implicit trust. Neurocomputing 237:92–100

    Article  Google Scholar 

  • Gong C, Tao D, Chang X et al (2019) Ensemble teaching for hybrid label propagation. IEEE Trans Cybern 49(2):388–402

    Article  Google Scholar 

  • Guan J, Xu M, Kong X (2018) Learning social regularized user representation in recommender system. Signal Process 144(3):306–310

    Article  Google Scholar 

  • Guo L, Ma J, Chen Z et al (2015) Learning to recommend with social context information from implicit feedback. Soft Comput 19(5):1351–1362

    Article  Google Scholar 

  • Gupta S, Kant V (2020) Credibility score based multi-criteria recommender system. Knowl-Based Syst 196(1):1–12

    Google Scholar 

  • He C, Parra D, Verbert K (2016) Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Syst Appl 56(9):9–27

    Article  Google Scholar 

  • He Y, Wang C, Jiang CJ (2018a) Correlated matrix factorization for recommendation with implicit feedback. IEEE Trans Knowl Data Eng 2018:1–15

    Google Scholar 

  • He Y, Wang C, Jiang C (2018b) Correlated matrix factorization for recommendation with implicit feedback. IEEE Trans Knowl Data Eng 31(3):451–464

    Article  Google Scholar 

  • Herce-Zelaya J, Porcel C, Bernabe-Moreno J et al (2020) New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Inf Sci 53:156–170

    Article  MathSciNet  Google Scholar 

  • Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM conference on recommender systems (RecSys’10), ACM, pp 26–30

  • Kong X, Mao M, Wang W (2018) VOPRec: Vector representation learning of papers with text information and structural identity for recommendation. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/TETC.2018.2830698

    Article  Google Scholar 

  • Li Y, Wang D, He H et al (2017a) Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems. Neurocomputing 249:48–63

    Article  Google Scholar 

  • Li J, Chen C, Chen H et al (2017b) Towards context-aware social recommendation via individual trust. Knowl-Based Syst 2017:58–66

    Article  Google Scholar 

  • Li H, Ma X, Shi J (2018) Incorporating trust relation with PMF to enhance social network recommendation performance. Int Pattern Recogn Artific Intell 30(6):113–124

    Article  Google Scholar 

  • Liu H, Jing L, Yu J (2017) Survey of matrix factorization based recommendation methods by integrating social information. J Softw 2017:1–24. https://doi.org/10.13328/j.cnki.jos.005391

    Article  Google Scholar 

  • Lu Q, Guo F (2019) Personalized information recommendation model based on context contribution and item correlation. Measurement 142:30–39

    Article  Google Scholar 

  • Luo X, Zhou M, Li S et al (2018) An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications. IEEE Trans Ind Inf 14(5):2011–2022

    Article  Google Scholar 

  • Ma H, Yang H, Lyu M R, et al (2008) SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of ACM conference on information & knowledge management (CIKM’08), pp 931–940

  • Ma H, King I, Lyu RM, et al (2009) Learning to recommend with social trust ensemble. In: Proceedings of 32nd international ACM SIGIR conference on research and development in information retrieval, pp 1–8

  • Meng X, Liu S, Zhang Y et al (2015) Research on social recommender systems. J Softw 26(6):1356–1372

    MathSciNet  Google Scholar 

  • Nabizadeh A, Leal J, Rafsanjani H et al (2020) (2020) Learning path personalization and recommendation methods: A survey of the state-of-the-art. Expert Syst Appl 159(9):1767–1776. https://doi.org/10.1016/j.eswa.2020.113596

    Article  Google Scholar 

  • Pan Y, He F, Yu H (2018) Social recommendation algorithm using implicit similarity in trust. Chinese J Comput 41(1):65–81

    Google Scholar 

  • Panagiotakis C, Papadakis H, Papagrigoriou A et al (2021) Improving recommender systems via a dual training error based correction approach. Expert Syst Appl 183(5):115386

    Article  Google Scholar 

  • Paradarami TK, Bastian ND, Wightman JL (2017) A hybrid recommender system using artificial neural networks. Exp Syst Appl 83:300–313

    Article  Google Scholar 

  • Pereira BL, Ueda A, Penha G, et al (2019) Online learning to rank for sequential music recommendation. In: Proceedings of the 13th ACM conference on recommender systems (RecSys’19). ACM, Copenhagen, Denmark. New York, NY, pp 237–245

  • Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97(1):205–227

    Article  Google Scholar 

  • Rafailidis D, Crestani F (2017) Learning to rank with trust and distrust in recommender systems. In: Proceedings of the 11th ACM conference on recommender systems (RecSys’17). ACM, pp 5–13

  • Rezaeimehr F, Moradi P, Ahmadian S (2017) TCARS: Time- and community-aware recommendation system. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2017.04.003

    Article  Google Scholar 

  • Ricci F, Rokach L, Shapira B, et al (2010) Recommender systems handbook: context-aware recommender systems. Springer, New York, pp 217–253

  • Sa A, Nj B, Mj B et al (2021) Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach. Expert Syst Appl 2021:1–15

    Google Scholar 

  • Salakhutdinov R, Mnih A (2008) Probabilistic matrix factorization. In Proceedings of NIPS

  • Sambhav Y, Vikash S et al (2018) Trust aware recommender system using swarm intelligence. J Comput Sci 28(1):180–192. https://doi.org/10.1016/j.jocs.2018.09.007

    Article  Google Scholar 

  • Sedhain S, Menon A K, Sanner S, Xie L, Braziunas D (2017) Low-rank linear cold-start recommendation from social data. In: Proceedings of the 31st AAAI conference on artificial intelligence. AAAI Press

  • Seo Y, Kim Y, Lee E et al (2017) Personalized recommender system based on friendship strength in social network services. Expert Syst Appl 69:135–148

    Article  Google Scholar 

  • Shneiderman B (2020) Human-centered artificial intelligence: reliable, safe & trustworthy. Int J Human-Comput Interact 36:495–504. https://doi.org/10.1080/10447318.2020.1741118

    Article  Google Scholar 

  • Shokeen J, Rana C (2020) Social recommender systems: techniques, domains, metrics, datasets and future scope. J Intell Inf Syst 54(2):1–35

    Google Scholar 

  • Tang J, Gao H, Hu X, et al (2013) Exploiting homophily effect for trust prediction. In: Proceedings of ACM international conference on web search and data mining (WSDM). ACM, pp 53–62

  • Wang Y, Wang X, Zuo W (2014) Trust prediction modeling based on social theories. J Softw 25(12):2893–2904

    MathSciNet  Google Scholar 

  • Wei J, He J, Chen K et al (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl 69:29–39

    Article  Google Scholar 

  • Yan S, Lin KJ, Zheng X et al (2017) An approach for building efficient and accurate social recommender systems using individual relationship networks. IEEE Trans Knowl Data Eng 29(10):2086–2099

    Article  Google Scholar 

  • Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41(1):1–10

    Article  Google Scholar 

  • Yang B, Yu L, Liu J et al (2017) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633–1647

    Article  Google Scholar 

  • Yao W, He J, Huang G, et al (2014) Modeling dual role preferences for trust-aware recommendation. In: Proceedings of International ACM SIGIR conference on research & development in information retrieval. ACM, pp 975–978

  • Yu W, Li S (2018) Recommender systems based on multiple social networks correlation. Futur Gener Comput Syst 87(1):312–327

    Article  Google Scholar 

  • Yu Y, Gao Y, Wang H (2018) Integrating user social status and matrix factorization for item recommendation. J Comput Res Dev 55(1):113–124

    Google Scholar 

  • Zhang Z, Xu G, Zhang P et al (2017) Personalized recommendation algorithm for social networks based on comprehensive trust. Appl Intell 47(3):659–669

    Article  Google Scholar 

  • Zhang W, Du Y, Yang Y et al (2018) DeRec: a data-driven approach to accurate recommendation with deep learning and weighted loss function. Electron Comm Res Appl 31:12–23

    Article  Google Scholar 

  • Zheng X, Luo Y (2018) Sun L (2018) A novel social network hybrid recommender system based on hypergraph topologic structure. World Wide Web-Int Web Inf Syst 21:985–1013

    Article  Google Scholar 

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Acknowledgements

We would like to thank the anonymous reviewers and editor for their helpful comments. This work was supported in part by the National Natural Science Foundation of China under Grants 61672471, 61975187, and 61802352, in part by the Industrial Science and Technology Research Project of Henan Province under Grants 212102210410, 212102310556, 202102210387, 202102210178, 212102210418, 222102210031, 222102110045, 222102210323, 222102210030, 222102210024, and 182102310969, in part by the Zhongyuan Science and Technology Innovation Leadership Program under Grant 214200510026, in part by the Natural Science Foundation Projectin of Henan Province under Grant 222300420582, in part by the Blue Book of Science Research Report on the "Belt and Road" Tourism Development Grant 2017sz01, in part by Shaanxi innovation capability support plan under Grant 2018KRM071, in part by the Doctoral Fund Project of Zhengzhou University of Light Industry under Grants 2020BSJJ030 and 2020BSJJ031, and in part by the innovation team of data science and knowledge engineering of Zhengzhou University of Light Industry under Grant 13606000032.

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Authors and Affiliations

  1. Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Rui Chen, Jian-wei Zhang, Zhifeng Zhang, Pu Li & Hui Liang

  2. Henan Joint International Research Laboratory of Computer Animation Implementation Technologies, Zhengzhou, China

    Rui Chen, Jian-wei Zhang, Zhifeng Zhang, Pu Li & Hui Liang

  3. Insititute for Silk Road Research, Xi’an University of Finance and Economics, Xi’an, 710100, China

    Yan-Shuo Chang

  4. Software College, Pingdingshan University, Pingdingshan, 467000, China

    Jingli Gao

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The content of this article has not been published, nor has it been submitted for consideration to other journals. There is no conflict of interest in the content of this article. With the consent of all the authors, this article will be authorized for publication. The contributions of each author in this article are as follows: Dr. Rui Chen wrote the article, Prof. Jian-wei Zhang revised the paper, Prof. Zhifeng Zhang put forward many valuable suggestions for this article, Dr. Jingli Gao verified the method and experiment, Dr. Pu Li completed the experiment of the paper, and Prof. Hui Liang revised the grammar of the paper.

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Correspondence to Jian-wei Zhang.

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Chen, R., Zhang, Jw., Zhang, Z. et al. A comprehensive social matrix factorization for recommendations with prediction and feedback mechanisms by fusing trust relationships and social tags. Soft Comput 26, 11479–11496 (2022). https://doi.org/10.1007/s00500-022-07440-x

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