TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Dictionary learning; Machine learning; Recommender systems; Sparse representation; Trust relationship; Cold-start; Data sparsity; Embeddings; Learning models; Learning techniques; Machine-learning; matrix; Engineering (all); Computer Science Applications; Artificial Intelligence; General Engineering

Abstract :

[en] Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster.

Disciplines :

Computer science

Author, co-author :

KHALEDIAN, Navid ^✱; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CritiX ; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

Nazari, Amin ; Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran

Khamforoosh, Keyhan ; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

Abualigah, Laith ; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

Javaheri, Danial ; Department of Computer Engineering, Chosun University, Gwangju, South Korea

^✱ These authors have contributed equally to this work.

External co-authors :

yes

Language :

English

Title :

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Publication date :

15 October 2023

Journal title :

Expert Systems with Applications

ISSN :

0957-4174

eISSN :

1873-6793

Publisher :

Elsevier Ltd

Volume :

228

Pages :

120487

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S0957417423009892?httpAccept=text/xml

Available on ORBilu :

since 21 November 2023

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Bibliography

Ahmadian, S., Joorabloo, N., Jalili, M., Ahmadian, M., Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach. Expert Systems with Applications, 187, 2022, 115849.
Alhijawi, B., Kilani, Y., A collaborative filtering recommender system using genetic algorithm. Information Processing & Management, 57(6), 2020, 102310.
Amestoy, P., Buttari, A., Higham, N., l'Excellent, J.-Y., Mary, T., & Vieuble, B. (2022). Combining sparse approximate factorizations with mixed precision iterative refinement, 49(1), 1–29.
Ar, Y., Bostanci, E., A genetic algorithm solution to the collaborative filtering problem. Expert Systems with Applications 61 (2016), 122–128.
Ayub, M., Ghazanfar, M.A., Mehmood, Z., Saba, T., Alharbey, R., Munshi, A.M., Alrige, M.A., Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems. PloS One, 14(8), 2019, e0220129.
Azadjalal, M.M., Moradi, P., Abdollahpouri, A., Jalili, M., A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowledge-Based Systems 116 (2017), 130–143.
Behera, G., Nain, N., Handling data sparsity via item metadata embedding into deep collaborative recommender system. Journal of King Saud University-Computer and Information Sciences 34:10 (2022), 9953–9963.
Behera, G., & Nain, N. (2022b). Trade-off between memory and model-based collaborative filtering recommender system. Proceedings of the international conference on paradigms of communication, computing and data sciences, 137-146.
Chen, G., Xu, C., Wang, J., Feng, J., Feng, J., Nonnegative matrix factorization for link prediction in directed complex networks using PageRank and asymmetric link clustering information. Expert Systems with Applications, 148, 2020, 113290.
Chen, L.-J., Gao, J., A trust-based recommendation method using network diffusion processes. Physica A: Statistical Mechanics and its Applications 506 (2018), 679–691.
Du, H., Zhang, Y., Ma, L., Zhang, F., Structured discriminant analysis dictionary learning for pattern classification. Knowledge-Based Systems, 216, 2021, 106794.
Feng, X., Wu, S., Tang, Z., Li, Z., Sparse latent model with dual graph regularization for collaborative filtering. Neurocomputing 284 (2018), 128–137.
Forouzandeh, S., Berahmand, K., Rostami, M., Presentation of a recommender system with ensemble learning and graph embedding: A case on MovieLens. Multimedia Tools and Applications 80:5 (2021), 7805–7832.
Forouzandeh, S., Rostami, M., Berahmand, K., Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: Effects of H-index centrality, similarity in items and friends. Engineering Applications of Artificial Intelligence, 104, 2021, 104325.
Forouzandeh, S., Rostami, M., Berahmand, K., A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and Topsis model. Fuzzy Information and Engineering, 2022, 1–25.
Geluvaraj, B., Sundaram, M., A hybrid approach to resolve data sparsity and cold start hassle in recommender systems. Pervasive Computing and Social Networking, 2022, Springer, 499–510.
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. Proceedings of the AAAI conference on artificial intelligence, 29(1), 123-129.
Guo, J., Zhou, Y., Zhang, P., Song, B., Chen, C., Trust-aware recommendation based on heterogeneous multi-relational graphs fusion. Information Fusion 74 (2021), 87–95.
Jakomin, M., Bosnić, Z., Curk, T., Simultaneous incremental matrix factorization for streaming recommender systems. Expert Systems with Applications, 160, 2020, 113685.
Jamali, M., & Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks. Proceedings of the fourth ACM conference on Recommender systems, 135-142.
Javaheri, D., Gorgin, S., Lee, J.-A., Masdari, M., An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing. Sustainable Computing: Informatics and Systems, 36, 2022, 100787, 10.1016/j.suscom.2022.100787.
Jing, X.-Y., Wu, F., Li, Z., Hu, R., Zhang, D., Multi-label dictionary learning for image annotation. IEEE Transactions on Image Processing 25:6 (2016), 2712–2725.
Kartoglu, I.E., Spratling, M.W., Two collaborative filtering recommender systems based on sparse dictionary coding. Knowledge and Information Systems 57:3 (2018), 709–720.
Khaledian, N., Mardukhi, F., CFMT: A collaborative filtering approach based on the nonnegative matrix factorization technique and trust relationships. Journal of Ambient Intelligence and Humanized Computing, 2022, 1–17.
Kiran, R., Kumar, P., Bhasker, B., DNNRec: A novel deep learning based hybrid recommender system. Expert Systems with Applications, 144, 2020, 113054.
Kordabadi, M., Nazari, A., Mansoorizadeh, M., A movie recommender system based on topic modeling using machine learning methods. International Journal of Web Research 5:2 (2022), 19–28, 10.22133/ijwr.2022.370251.1139.
Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 426-434.
Kuo, R., Chen, C.-K., Keng, S.-H., Application of hybrid metaheuristic with perturbation-based K-nearest neighbors algorithm and densest imputation to collaborative filtering in recommender systems. Information Sciences 575 (2021), 90–115.
Lee, J., Kim, S., Lebanon, G., Singer, Y., Local low-rank matrix approximation. International Conference on Machine Learning, 2013, 82–90.
Li, H., Li, K., An, J., Zheng, W., Li, K., An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs. Information Sciences 496 (2019), 464–484.
Li, J., Tao, J., Ding, W., Zhang, J., Meng, Z., Period-assisted adaptive parameterized wavelet dictionary and its sparse representation for periodic transient features of rolling bearing faults. Mechanical Systems and Signal Processing, 169, 2022, 108796.
Li, W., Mo, J., Xin, M., Jin, Q., An Optimized trust model integrated with linear features for cyber-enabled recommendation services. Journal of Parallel and Distributed Computing 118 (2018), 81–88.
Li, W., Ye, Z., Xin, M., Jin, Q., Social recommendation based on trust and influence in SNS environments. Multimedia Tools and Applications 76:9 (2017), 11585–11602.
Li, W., Zhou, X., Shimizu, S., Xin, M., Jiang, J., Gao, H., Jin, Q., Personalization recommendation algorithm based on trust correlation degree and matrix factorization. IEEE Access 7 (2019), 45451–45459.
Livne, A., Tov, E.S., Solomon, A., Elyasaf, A., Shapira, B., Rokach, L., Evolving context-aware recommender systems with users in mind. Expert Systems with Applications, 189, 2022, 116042.
Luo, X., Zhou, M., Xia, Y., Zhu, Q., An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics 10:2 (2014), 1273–1284.
Mnih, A., Salakhutdinov, R.R., Probabilistic matrix factorization. Advances in Neural Information Processing Systems 20 (2008), 1257–1264.
Nazari, A., Kordabadi, M., Mansoorizadeh, M., Scalable and data-independent multi-agent recommender system using social networks analysis. International Journal of Information Technology & Decision Making 22:4 (2023), 1–22, 10.1142/s021962202350030x.
Papadakis, H., Papagrigoriou, A., Panagiotakis, C., Kosmas, E., Fragopoulou, P., Collaborative filtering recommender systems taxonomy. Knowledge and Information Systems 64:1 (2022), 35–74.
Parvin, H., Moradi, P., Esmaeili, S., TCFACO: Trust-aware collaborative filtering method based on ant colony optimization. Expert Systems with Applications 118 (2019), 152–168.
Parvin, H., Moradi, P., Esmaeili, S., Qader, N.N., A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method. Knowledge-Based Systems 166 (2019), 92–107.
Permiakova, O., Burger, T., Sketched Stochastic Dictionary Learning for large-scale data and application to high-throughput mass spectrometry. Statistical Analysis and Data Mining: The ASA Data Science Journal 15:1 (2022), 43–56.
Qi, Z., Yue, K., Duan, L., Wang, J., Qiao, S., Fu, X., Matrix factorization based Bayesian network embedding for efficient probabilistic inferences. Expert Systems with Applications, 169, 2021, 114294.
Rashidi, R., Khamforoosh, K., Sheikhahmadi, A., Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems. Electronic Commerce Research, 2021, 1–26.
Rodpysh, K.V., Mirabedini, S.J., Banirostam, T., Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems. Electronic Commerce Research, 2021, 1–27.
Tahmasbi, H., Jalali, M., Shakeri, H., TSCMF: Temporal and social collective matrix factorization model for recommender systems. Journal of Intelligent Information Systems 56:1 (2021), 169–187.
Talmon, R., Mallat, S., Zaveri, H., Coifman, R.R., Manifold learning for latent variable inference in dynamical systems. IEEE Transactions on Signal Processing 63:15 (2015), 3843–3856.
Wang, J., Zhu, L., Dai, T., Xu, Q., Gao, T., Low-rank and sparse matrix factorization with prior relations for recommender systems. Applied Intelligence 51 (2021), 3435–3449.
Wang, W., Yan, Y., Nie, F., Yan, S., Sebe, N., Flexible manifold learning with optimal graph for image and video representation. IEEE Transactions on Image Processing 27:6 (2018), 2664–2675.
Yang, B., Lei, Y., Liu, J., Li, W., Social collaborative filtering by trust. IEEE Transactions on Pattern Analysis and Machine Intelligence 39:8 (2016), 1633–1647.
Zhang, F., Qi, S., Liu, Q., Mao, M., Zeng, A., Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks. Expert Systems with Applications, 149, 2020, 113346.
Zhou, L., Du, G., Lü, K., Wang, L., A network-based sparse and multi-manifold regularized multiple non-negative matrix factorization for multi-view clustering. Expert Systems with Applications, 174, 2021, 114783.
Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S., Image reconstruction by domain-transform manifold learning. Nature 555:7697 (2018), 487–492.