{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:09:36Z","timestamp":1786982976886,"version":"3.56.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s41060-024-00698-4","type":"journal-article","created":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T11:28:29Z","timestamp":1734262109000},"page":"3851-3867","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Social recommendation system based on heterogeneous graph attention networks"],"prefix":"10.1007","volume":"20","author":[{"given":"Driss","family":"El Alaoui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamal","family":"Riffi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelouahed","family":"Sabri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Badraddine","family":"Aghoutane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Yahyaouy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Tairi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"698_CR1","doi-asserted-by":"crossref","unstructured":"Gheraibia, M.Y., Gouin-Vallerand, C.: Intelligent mobile-based recommender system framework for smart freight transport. In: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good (2019)","DOI":"10.1145\/3342428.3342697"},{"key":"698_CR2","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s10462-021-10043-x","volume":"55","author":"S Raza","year":"2020","unstructured":"Raza, S., Ding, C.: News recommender system: a review of recent progress, challenges, and opportunities. Artif. Intell. Rev. 55, 749\u2013800 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"698_CR3","unstructured":"Yi, K., Yamagishi, R., Li, T., Bai, Z., Ma, Q.: Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating. arXiv:abs\/2110.0 (2021)"},{"key":"698_CR4","doi-asserted-by":"crossref","unstructured":"Zou, J., Chen, Y., Kanoulas, E.: Towards question-based recommender systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)","DOI":"10.1145\/3397271.3401180"},{"key":"698_CR5","unstructured":"Alaoui, D.E., Riffi, J., Aghoutane, B., Sabri, M.A., Yahyaouy, A., Tairi, H.: Collaborative filtering: comparative study between matrix factorization and neural network method. In: International Conference on Networked Systems (2020)"},{"key":"698_CR6","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"698_CR7","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv:abs\/1703.0 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"698_CR8","unstructured":"Wu, S., Zhang, W., Sun, F., Cui, B.: Graph Neural Networks in Recommender Systems: A Survey. arXiv:abs\/2011.0 (2020)"},{"key":"698_CR9","unstructured":"Visnovsky, J., Kass\u00e1k, O., Kompan, M., Bielikov\u00e1, M.: The Cold-start Problem: Minimal Users\u2019 Activity Estimation. arXiv:abs\/2106.0 (2021)"},{"key":"698_CR10","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1086\/208570","volume":"17","author":"PM Herr","year":"1991","unstructured":"Herr, P.M., Kardes, F., Kim, J.: Effects of word-of-mouth and product-attribute information on persuasion: an accessibility\u2013diagnosticity perspective. J. Consum. Res. 17, 454\u2013462 (1991)","journal-title":"J. Consum. Res."},{"key":"698_CR11","unstructured":"Sharma, K., Lee, Y.-C., Nambi, S., Salian, A., Shah, S., Kim, S.-W., Kumar, S.: A Survey of Graph Neural Networks for Social Recommender Systems. arXiv:abs\/2212.0 (2022)"},{"key":"698_CR12","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/TPAMI.2016.2605085","volume":"39","author":"B Yang","year":"2013","unstructured":"Yang, B., Lei, Y., Liu, D.-Y., Liu, J.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1633\u20131647 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"698_CR13","doi-asserted-by":"crossref","unstructured":"Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: International Conference on Information and Knowledge Management (2008)","DOI":"10.1145\/1458082.1458205"},{"key":"698_CR14","doi-asserted-by":"crossref","unstructured":"Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135\u2013142 (2010)","DOI":"10.1145\/1864708.1864736"},{"key":"698_CR15","doi-asserted-by":"crossref","unstructured":"Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17\u201324 (2007)","DOI":"10.1145\/1297231.1297235"},{"key":"698_CR16","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Nie, L., Chua, T.-S.: Item silk road: recommending items from information domains to social users. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017)","DOI":"10.1145\/3077136.3080771"},{"key":"698_CR17","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TMM.2017.2740022","volume":"20","author":"Z Zhao","year":"2018","unstructured":"Zhao, Z., Yang, Q., Lu, H., Weninger, T., Cai, D., He, X., Zhuang, Y.: Social-aware movie recommendation via multimodal network learning. IEEE Trans. Multimed. 20, 430\u2013440 (2018)","journal-title":"IEEE Trans. Multimed."},{"key":"698_CR18","doi-asserted-by":"crossref","unstructured":"Tegene, A., Liu, Q., Muhammed, S.B., Leka, H.L.: Deep learning based matrix factorization for collaborative filtering. In: 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 165\u2013170 (2021)","DOI":"10.1109\/ICCWAMTIP53232.2021.9674157"},{"key":"698_CR19","doi-asserted-by":"crossref","unstructured":"Fan, W., Li, Q., Cheng, M.-C.: Deep modeling of social relations for recommendation. In: AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12132"},{"key":"698_CR20","unstructured":"Wang, J., Zhang, S., Xiao, Y., Song, R.: A Review on Graph Neural Network Methods in Financial Applications. arXiv:abs\/2111.1 (2021)"},{"key":"698_CR21","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1561\/2200000096","volume":"16","author":"L Wu","year":"2021","unstructured":"Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., Long, B.: Graph neural networks for natural language processing: a survey. Found. Trends Mach. Learn. 16, 119\u2013328 (2021)","journal-title":"Found. Trends Mach. Learn."},{"key":"698_CR22","unstructured":"Wang, Y., Li, Z., Farimani, A.B.: Graph Neural Networks for Molecules. arXiv:abs\/2209.0 (2022)"},{"key":"698_CR23","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s43246-022-00315-6","volume":"3","author":"P Reiser","year":"2022","unstructured":"Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., Hoesel, C., Schopmans, H., Sommer, T., Friederich, P.: Graph neural networks for materials science and chemistry. Commun. Mater. 3, 66 (2022)","journal-title":"Commun. Mater."},{"key":"698_CR24","unstructured":"Han, K., Wang, Y., Guo, J., Tang, Y., Wu, E.: Vision GNN: An Image is Worth Graph of Nodes. arXiv:abs\/2206.0 (2022)"},{"key":"698_CR25","doi-asserted-by":"crossref","unstructured":"Pradhyumna, P., Shreya, G.M.: Graph neural network (GNN) in image and video understanding using deep learning for computer vision applications. In: 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 1183\u20131189 (2021)","DOI":"10.1109\/ICESC51422.2021.9532631"},{"key":"698_CR26","unstructured":"Kim, J.J., Kim, J.: Representation Learning with Graph Neural Networks for Speech Emotion Recognition. arXiv:abs\/2208.0 (2022)"},{"key":"698_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, S., Qin, Y., Sun, K., Lin, Y.: Few-shot audio classification with attentional graph neural networks. In: Interspeech (2019)","DOI":"10.21437\/Interspeech.2019-1532"},{"key":"698_CR28","doi-asserted-by":"crossref","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, Y.E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: The World Wide Web Conference (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"698_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zhang, H., Gao, X., He, P., Weng, P., Gao, H., Chen, G.: Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The World Wide Web Conference (2019)","DOI":"10.1145\/3308558.3313442"},{"key":"698_CR30","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)","DOI":"10.1145\/3397271.3401063"},{"key":"698_CR31","doi-asserted-by":"publisher","first-page":"4753","DOI":"10.1109\/TKDE.2020.3048414","volume":"34","author":"L Wu","year":"2020","unstructured":"Wu, L., Li, J., Sun, P., Hong, R., Ge, Y., Wang, M.: DiffNet++: a neural influence and interest diffusion network for social recommendation. IEEE Trans. Knowl. Data Eng. 34, 4753\u20134766 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"698_CR32","doi-asserted-by":"crossref","unstructured":"Chang, J., Gao, C., He, X., Li, Y., Jin, D.: Bundle recommendation with graph convolutional networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)","DOI":"10.1145\/3397271.3401198"},{"key":"698_CR33","doi-asserted-by":"publisher","first-page":"11679","DOI":"10.1007\/s00521-022-07059-x","volume":"34","author":"DE Alaoui","year":"2022","unstructured":"Alaoui, D.E., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network. Neural Comput. Appl. 34, 11679\u201311690 (2022)","journal-title":"Neural Comput. Appl."},{"key":"698_CR34","doi-asserted-by":"crossref","unstructured":"Li, C., Jia, K., Shen, D., Shi, C.-J.R., Yang, H.: Hierarchical representation learning for bipartite graphs. In: IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/398"},{"key":"698_CR35","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based Recommendation with Graph Neural Networks. arXiv:abs\/1811.0 (2018)"},{"key":"698_CR36","unstructured":"Gupta, P., Garg, D., Malhotra, P., Vig, L., Shroff, G.M.: NISER: Normalized Item and Session Representations with Graph Neural Networks. arXiv:abs\/1909.0 (2019)"},{"key":"698_CR37","doi-asserted-by":"crossref","unstructured":"Lim, N., Hooi, B., Ng, S.-K., Wang, X., Goh, Y.L., Weng, R., Varadarajan, J.: STP-UDGAT: spatial-temporal-preference user dimensional graph attention network for next POI recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)","DOI":"10.1145\/3340531.3411876"},{"key":"698_CR38","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., Chua, T.-S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"698_CR39","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tang, S., Lei, Y., Song, W., Wang, S., Zhang, M.: DisenHAN: disentangled heterogeneous graph attention network for recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)","DOI":"10.1145\/3340531.3411996"},{"key":"698_CR40","doi-asserted-by":"crossref","unstructured":"El\u00a0Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Contextual recommendations: dynamic graph attention networks with edge adaptation. In: IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3477956"},{"issue":"4","key":"698_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3626243","volume":"15","author":"D Yu","year":"2024","unstructured":"Yu, D., Wang, X., Xiong, Y., Shen, X., Wu, R., Wang, D., Zou, Z., Xu, G.: Mhaner: a multi-source heterogeneous graph attention network for explainable recommendation in online games. ACM Trans. Intell. Syst. Technol. 15(4), 1\u201323 (2024)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"698_CR42","doi-asserted-by":"crossref","unstructured":"Li, A., Yang, B., Huo, H., Hussain, F.K., Xu, G.: Structure-and logic-aware heterogeneous graph learning for recommendation. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 544\u2013556. IEEE (2024)","DOI":"10.1109\/ICDE60146.2024.00048"},{"issue":"1","key":"698_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2438-1","volume":"18","author":"Y Jiang","year":"2024","unstructured":"Jiang, Y., Ma, H., Zhang, X., Li, Z., Chang, L.: Incorporating metapath interaction on heterogeneous information network for social recommendation. Front. Comput. Sci. 18(1), 181302 (2024)","journal-title":"Front. Comput. Sci."},{"key":"698_CR44","doi-asserted-by":"crossref","unstructured":"Yan, B., Cao, Y., Wang, H., Yang, W., Du, J., Shi, C.: Federated heterogeneous graph neural network for privacy-preserving recommendation. In: Proceedings of the ACM on Web Conference 2024, pp. 3919\u20133929 (2024)","DOI":"10.1145\/3589334.3645693"},{"issue":"4","key":"698_CR45","first-page":"1","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interactive Intell. Syst. 5(4), 1\u201319 (2015)","journal-title":"ACM Trans. Interactive Intell. Syst."},{"issue":"5","key":"698_CR46","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1007\/s10618-022-00859-8","volume":"36","author":"P Magron","year":"2022","unstructured":"Magron, P., F\u00e9votte, C.: Neural content-aware collaborative filtering for cold-start music recommendation. Data Min. Knowl. Discov. 36(5), 1971\u20132005 (2022)","journal-title":"Data Min. Knowl. Discov."},{"key":"698_CR47","doi-asserted-by":"crossref","unstructured":"Chang, B., Park, Y., Park, D., Kim, S., Kang, J.: Content-aware hierarchical point-of-interest embedding model for successive poi recommendation. In: IJCAI, vol. 20, pp. 3301\u20133307 (2018)","DOI":"10.24963\/ijcai.2018\/458"},{"key":"698_CR48","unstructured":"Reddy, S., Reddy, K.T., Kumari, V.: Optimization of Deep Learning Using Various Optimizers, Loss Functions and Dropout (2019)"},{"key":"698_CR49","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1145\/963770.963776","volume":"22","author":"M Deshpande","year":"2004","unstructured":"Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22, 143\u2013177 (2004)","journal-title":"ACM Trans. Inf. Syst."},{"key":"698_CR50","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20, 422\u2013446 (2002)","journal-title":"ACM Trans. Inf. Syst."},{"key":"698_CR51","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI (2009)"},{"key":"698_CR52","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.-S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"698_CR53","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, S., Tao, Y., Sun, J., Liu, X.-l., Yu, P.S., Wang, T.: DGRec: graph neural network for recommendation with diversified embedding generation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (2022)","DOI":"10.1145\/3539597.3570472"},{"key":"698_CR54","doi-asserted-by":"crossref","unstructured":"Bai, T., Zhang, Y., Wu, B., Nie, J.-Y.: Temporal graph neural networks for social recommendation. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 898\u2013903 (2020)","DOI":"10.1109\/BigData50022.2020.9378444"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00698-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-024-00698-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00698-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:54:15Z","timestamp":1758797655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-024-00698-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["698"],"URL":"https:\/\/doi.org\/10.1007\/s41060-024-00698-4","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,15]]},"assertion":[{"value":"16 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Custom code.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}