{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:39:48Z","timestamp":1740123588004,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972337"],"award-info":[{"award-number":["61972337"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11227-023-05113-6","type":"journal-article","created":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T03:02:37Z","timestamp":1677898957000},"page":"11934-11964","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metapath-guided dual semantic-aware filtering for HIN-based recommendation"],"prefix":"10.1007","volume":"79","author":[{"given":"Surong","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haosen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruilin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"5113_CR1","unstructured":"van\u00a0den Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv:abs\/1706.02263"},{"issue":"5","key":"5113_CR2","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.1109\/TNNLS.2020.3044146","volume":"33","author":"FM Bianchi","year":"2022","unstructured":"Bianchi FM, Grattarola D, Livi L et al (2022) Hierarchical representation learning in graph neural networks with node decimation pooling. IEEE Trans Neural Netw Learn Syst 33(5):2195\u20132207. https:\/\/doi.org\/10.1109\/TNNLS.2020.3044146","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5113_CR3","doi-asserted-by":"publisher","unstructured":"Chen H, Yin H, Wang W et al (2018) PME: projected metric embedding on heterogeneous networks for link prediction. In: Guo Y, Farooq F (eds) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018. ACM, pp 1177\u20131186, https:\/\/doi.org\/10.1145\/3219819.3219986","DOI":"10.1145\/3219819.3219986"},{"key":"5113_CR4","doi-asserted-by":"publisher","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017. ACM, pp 135\u2013144, https:\/\/doi.org\/10.1145\/3097983.3098036","DOI":"10.1145\/3097983.3098036"},{"key":"5113_CR5","doi-asserted-by":"publisher","unstructured":"Fan S, Zhu J, Han X et al (2019a) Metapath-guided heterogeneous graph neural network for intent recommendation. In: Teredesai A, Kumar V, Li Y et al (eds) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019. ACM, pp 2478\u20132486, https:\/\/doi.org\/10.1145\/3292500.3330673","DOI":"10.1145\/3292500.3330673"},{"key":"5113_CR6","doi-asserted-by":"publisher","unstructured":"Fan W, Ma Y, Li Q et al (2019b) Graph neural networks for social recommendation. In: Liu L, White RW, Mantrach A et al (eds) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. ACM, pp 417\u2013426, https:\/\/doi.org\/10.1145\/3308558.3313488,","DOI":"10.1145\/3308558.3313488"},{"key":"5113_CR7","doi-asserted-by":"publisher","unstructured":"Feng Y, Hu B, Lv F et al (2020) ATBRG: adaptive target-behavior relational graph network for effective recommendation. In: Huang J, Chang Y, Cheng X et al (eds) Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020. ACM, pp 2231\u20132240, https:\/\/doi.org\/10.1145\/3397271.3401428","DOI":"10.1145\/3397271.3401428"},{"key":"5113_CR8","doi-asserted-by":"publisher","unstructured":"Fu X, Zhang J, Meng Z et al (2020) MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: Huang Y, King I, Liu T et al (eds) WWW \u201920: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020. ACM \/ IW3C2, pp 2331\u20132341, https:\/\/doi.org\/10.1145\/3366423.3380297,","DOI":"10.1145\/3366423.3380297"},{"key":"5113_CR9","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Teh YW, Titterington DM (eds) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, May 13-15, 2010, JMLR Proceedings, vol\u00a09. JMLR.org, pp 249\u2013256, http:\/\/proceedings.mlr.press\/v9\/glorot10a.html"},{"key":"5113_CR10","doi-asserted-by":"publisher","unstructured":"Gong J, Wang S, Wang J et al (2020) Attentional graph convolutional networks for knowledge concept recommendation in moocs in a heterogeneous view. In: Huang J, Chang Y, Cheng X et al (eds) Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020. ACM, pp 79\u201388, https:\/\/doi.org\/10.1145\/3397271.3401057","DOI":"10.1145\/3397271.3401057"},{"key":"5113_CR11","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017a) Inductive representation learning on large graphs. In: Guyon I, von Luxburg U, Bengio S et al (eds) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 1024\u20131034, https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html"},{"key":"5113_CR12","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017b) Inductive representation learning on large graphs. In: Guyon I, von Luxburg U, Bengio S et al (eds) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 1024\u20131034, https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html"},{"key":"5113_CR13","doi-asserted-by":"publisher","unstructured":"He X, Deng K, Wang X et al (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Huang J, Chang Y, Cheng X et al (eds) Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020. ACM, pp 639\u2013648, https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"key":"5113_CR14","doi-asserted-by":"publisher","unstructured":"He Y, Song Y, Li J et al (2019) Hetespaceywalk: A heterogeneous spacey random walk for heterogeneous information network embedding. In: Zhu W, Tao D, Cheng X et al (eds) Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019. ACM, pp 639\u2013648, https:\/\/doi.org\/10.1145\/3357384.3358061","DOI":"10.1145\/3357384.3358061"},{"key":"5113_CR15","doi-asserted-by":"crossref","unstructured":"Hei Y, Yang R, Peng H et al (2021) HAWK: rapid android malware detection through heterogeneous graph attention networks. arXiv:abs\/2108.07548","DOI":"10.1109\/TNNLS.2021.3105617"},{"key":"5113_CR16","doi-asserted-by":"publisher","unstructured":"Hu B, Shi C, Zhao WX et al (2018) Leveraging meta-path based context for top- N recommendation with A neural co-attention model. In: Guo Y, Farooq F (eds) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018. ACM, pp 1531\u20131540, https:\/\/doi.org\/10.1145\/3219819.3219965","DOI":"10.1145\/3219819.3219965"},{"key":"5113_CR17","doi-asserted-by":"publisher","unstructured":"Hu B, Fang Y, Shi C (2019) Adversarial learning on heterogeneous information networks. In: Teredesai A, Kumar V, Li Y et al (eds) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019. ACM, pp 120\u2013129, https:\/\/doi.org\/10.1145\/3292500.3330970","DOI":"10.1145\/3292500.3330970"},{"key":"5113_CR18","unstructured":"Huang Z, Mamoulis N (2017) Heterogeneous information network embedding for meta path based proximity. arXiv:abs\/1701.05291"},{"key":"5113_CR19","doi-asserted-by":"publisher","unstructured":"Hussein R, Yang D, Cudr\u00e9-Mauroux P (2018) Are meta-paths necessary?: Revisiting heterogeneous graph embeddings. In: Cuzzocrea A, Allan J, Paton NW et al (eds) Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018. ACM, pp 437\u2013446, https:\/\/doi.org\/10.1145\/3269206.3271777","DOI":"10.1145\/3269206.3271777"},{"key":"5113_CR20","doi-asserted-by":"publisher","unstructured":"Ji H, Shi C, Wang B (2018) Attention based meta path fusion for heterogeneous information network embedding. In: Geng X, Kang B (eds) PRICAI 2018: Trends in Artificial Intelligence - 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, August 28-31, 2018, Proceedings, Part I, Lecture Notes in Computer Science, vol 11012. Springer, pp 348\u2013360, https:\/\/doi.org\/10.1007\/978-3-319-97304-3_27","DOI":"10.1007\/978-3-319-97304-3_27"},{"key":"5113_CR21","doi-asserted-by":"publisher","unstructured":"Jin J, Qin J, Fang Y et al (2020) An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In: Gupta R, Liu Y, Tang J et al (eds) KDD \u201920: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020. ACM, pp 75\u201384, https:\/\/doi.org\/10.1145\/3394486.3403050","DOI":"10.1145\/3394486.3403050"},{"key":"5113_CR22","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: Bengio Y, LeCun Y (eds) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, arXiv:abs\/1412.6980"},{"key":"5113_CR23","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"5113_CR24","doi-asserted-by":"publisher","unstructured":"Lee S, Park C, Yu H (2019) Bhin2vec: Balancing the type of relation in heterogeneous information network. In: Zhu W, Tao D, Cheng X et al (eds) Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019. ACM, pp 619\u2013628, https:\/\/doi.org\/10.1145\/3357384.3357893","DOI":"10.1145\/3357384.3357893"},{"key":"5113_CR25","unstructured":"Li Y, Tarlow D, Brockschmidt M et al (2016) Gated graph sequence neural networks. In: Bengio Y, LeCun Y (eds) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, arXiv:abs\/1511.05493"},{"issue":"109","key":"5113_CR26","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.knosys.2022.109185","volume":"251","author":"T Liang","year":"2022","unstructured":"Liang T, Ma L, Zhang W et al (2022) Content-aware recommendation via dynamic heterogeneous graph convolutional network. Knowl Based Syst 251(109):185. https:\/\/doi.org\/10.1016\/j.knosys.2022.109185","journal-title":"Knowl Based Syst"},{"key":"5113_CR27","doi-asserted-by":"publisher","unstructured":"Ma H, Zhou D, Liu C et al (2011) Recommender Systems with Social Regularization. In: King I, Nejdl W, Li H (eds) Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, February 9-12, 2011. ACM, pp 287\u2013296, https:\/\/doi.org\/10.1145\/1935826.1935877","DOI":"10.1145\/1935826.1935877"},{"key":"5113_CR28","unstructured":"Martins AFT, Astudillo RF (2016) From softmax to sparsemax: A Sparse Model of Attention and Multi-label Classification. In: Balcan M, Weinberger KQ (eds) Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, JMLR Workshop and Conference Proceedings, vol\u00a048. JMLR.org, pp 1614\u20131623, http:\/\/proceedings.mlr.press\/v48\/martins16.html"},{"key":"5113_CR29","doi-asserted-by":"publisher","unstructured":"Pang Y, Wu L, Shen Q et al (2022) Heterogeneous Global Graph neural Networks for Personalized Session-based Recommendation. In: WSDM \u201922: The Fifteenth ACM International Conference on Web Search and Data Mining, virtual event \/ Tempe, AZ, USA, February 21 - 25, 2022. ACM, pp 775\u2013783, https:\/\/doi.org\/10.1145\/3488560.3498505","DOI":"10.1145\/3488560.3498505"},{"key":"5113_CR30","doi-asserted-by":"publisher","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online Learning of Social Representations. In: Macskassy SA, Perlich C, Leskovec J et al (eds) The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201914, New York, NY, USA - August 24 - 27, 2014. ACM, pp 701\u2013710, https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"5113_CR31","unstructured":"Qian T, Liang Y, Li Q et al (2020) Attribute graph neural networks for strict cold start recommendation. IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"5113_CR32","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10844-021-00650-z","volume":"58","author":"JAP Sacenti","year":"2022","unstructured":"Sacenti JAP, Fileto R, Willrich R (2022) Knowledge graph summarization impacts on movie recommendations. J Intell Inf Syst 58(1):43\u201366. https:\/\/doi.org\/10.1007\/s10844-021-00650-z","journal-title":"J Intell Inf Syst"},{"issue":"10","key":"5113_CR33","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.1109\/TKDE.2013.2297920","volume":"26","author":"C Shi","year":"2014","unstructured":"Shi C, Kong X, Huang Y et al (2014) Hetesim: a general framework for relevance measure in heterogeneous networks. IEEE Trans Knowl Data Eng 26(10):2479\u20132492. https:\/\/doi.org\/10.1109\/TKDE.2013.2297920","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5113_CR34","doi-asserted-by":"publisher","unstructured":"Shi C, Zhang Z, Luo P et al (2015) Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks. In: Bailey J, Moffat A, Aggarwal CC et al (eds) Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015. ACM, pp 453\u2013462, https:\/\/doi.org\/10.1145\/2806416.2806528","DOI":"10.1145\/2806416.2806528"},{"issue":"1","key":"5113_CR35","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","volume":"29","author":"C Shi","year":"2017","unstructured":"Shi C, Li Y, Zhang J et al (2017) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17\u201337. https:\/\/doi.org\/10.1109\/TKDE.2016.2598561","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"5113_CR36","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2019","unstructured":"Shi C, Hu B, Zhao WX et al (2019) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357\u2013370. https:\/\/doi.org\/10.1109\/TKDE.2018.2833443","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5113_CR37","unstructured":"Shi J, Ji H, Shi C et al (2020) Heterogeneous graph neural network for recommendation. arXiv:abs\/2009.00799"},{"key":"5113_CR38","unstructured":"Su\u00e1rez J, Garc\u00eda S, Herrera F (2018) A tutorial on distance metric learning: Mathematical foundations, algorithms and software. arXiv:abs\/1812.05944"},{"key":"5113_CR39","doi-asserted-by":"publisher","unstructured":"Sun Q, Peng H, Li J et al (2020) Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks. In: Plant C, Wang H, Cuzzocrea A et al (eds) 20th IEEE International Conference on Data Mining, ICDM 2020, Sorrento, Italy, November 17-20, 2020. IEEE, pp 511\u2013520, https:\/\/doi.org\/10.1109\/ICDM50108.2020.00060","DOI":"10.1109\/ICDM50108.2020.00060"},{"key":"5113_CR40","doi-asserted-by":"crossref","unstructured":"Sun Y, Han J, Yan X et al (2011) Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992\u20131003. http:\/\/www.vldb.org\/pvldb\/vol4\/p992-sun.pdf","DOI":"10.14778\/3402707.3402736"},{"key":"5113_CR41","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S et al (eds) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 5998\u20136008, https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"5113_CR42","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et al (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"issue":"3","key":"5113_CR43","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1109\/TNNLS.2020.2984665","volume":"32","author":"D Wang","year":"2021","unstructured":"Wang D, Zhang X, Yu D et al (2021) CAME: content- and context-aware music embedding for recommendation. IEEE Trans Neural Networks Learn Syst 32(3):1375\u20131388. https:\/\/doi.org\/10.1109\/TNNLS.2020.2984665","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"5113_CR44","doi-asserted-by":"publisher","unstructured":"Wang X, He X, Wang M et al (2019a) Neural graph collaborative filtering. In: Piwowarski B, Chevalier M, Gaussier \u00c9 et al (eds) Proceedings of the 42nd International ACM Sigir Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019. ACM, pp 165\u2013174, https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"key":"5113_CR45","doi-asserted-by":"publisher","unstructured":"Wang X, Ji H, Shi C et al (2019b) Heterogeneous graph attention network. In: Liu L, White RW, Mantrach A et al (eds) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. ACM, pp 2022\u20132032, https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"5113_CR46","doi-asserted-by":"crossref","unstructured":"Wang Z, Liu H, Du Y et al (2019c) Unified Embedding Model Over Heterogeneous Information Network for Personalized Recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp 3813\u20133819","DOI":"10.24963\/ijcai.2019\/529"},{"key":"5113_CR47","doi-asserted-by":"publisher","unstructured":"Wu L, Sun P, Fu Y et al (2019a) A Neural Influence Diffusion Model for Social Recommendation. In: Piwowarski B, Chevalier M, Gaussier \u00c9 et al (eds) Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019. ACM, pp 235\u2013244, https:\/\/doi.org\/10.1145\/3331184.3331214","DOI":"10.1145\/3331184.3331214"},{"key":"5113_CR48","unstructured":"Wu L, Li J, Sun P et al (2020a) Diffnet++: A neural influence and interest diffusion network for social recommendation. arXiv:abs\/2002.00844"},{"key":"5113_CR49","doi-asserted-by":"publisher","unstructured":"Wu Q, Zhang H, Gao X et al (2019b) Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. In: Liu L, White RW, Mantrach A et al (eds) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. ACM, pp 2091\u20132102, https:\/\/doi.org\/10.1145\/3308558.3313442","DOI":"10.1145\/3308558.3313442"},{"key":"5113_CR50","unstructured":"Wu S, Zhang W, Sun F et al (2020b) Graph neural networks in recommender systems: A survey. arXiv:abs\/2011.02260"},{"key":"5113_CR51","doi-asserted-by":"publisher","unstructured":"Xu L, Wei X, Cao J et al (2017) Embedding of embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks. In: de\u00a0Rijke M, Shokouhi M, Tomkins A et al (eds) Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, February 6-10, 2017. ACM, pp 741\u2013749, https:\/\/doi.org\/10.1145\/3018661.3018723","DOI":"10.1145\/3018661.3018723"},{"issue":"114","key":"5113_CR52","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.eswa.2021.114601","volume":"174","author":"S Yan","year":"2021","unstructured":"Yan S, Wang H, Li Y et al (2021) Attention-aware metapath-based network embedding for HIN based recommendation. Expert Syst Appl 174(114):601. https:\/\/doi.org\/10.1016\/j.eswa.2021.114601","journal-title":"Expert Syst Appl"},{"key":"5113_CR53","doi-asserted-by":"crossref","unstructured":"Yang Y, Guan Z, Li J et al (2021) Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2021.3101356"},{"key":"5113_CR54","doi-asserted-by":"publisher","unstructured":"Ying R, He R, Chen K et al (2018a) Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In: Guo Y, Farooq F (eds) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018. ACM, pp 974\u2013983, https:\/\/doi.org\/10.1145\/3219819.3219890","DOI":"10.1145\/3219819.3219890"},{"key":"5113_CR55","doi-asserted-by":"publisher","unstructured":"Ying R, He R, Chen K et al (2018b) Graph Convolutional Neural Networks for Web-scale Recommender Systems. In: Proceedings of the 24th ACM sigkdd International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018. ACM, pp 974\u2013983, https:\/\/doi.org\/10.1145\/3219819.3219890","DOI":"10.1145\/3219819.3219890"},{"key":"5113_CR56","doi-asserted-by":"publisher","unstructured":"Zhang D, Yin J, Zhu X et al (2018) Metagraph2vec: Complex Semantic Path Augmented Heterogeneous Network Embedding. In: Phung DQ, Tseng VS, Webb GI et al (eds) Advances in knowledge discovery and data mining - 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part II, Lecture Notes in Computer Science, vol 10938. Springer, pp 196\u2013208, https:\/\/doi.org\/10.1007\/978-3-319-93037-4_16","DOI":"10.1007\/978-3-319-93037-4_16"},{"key":"5113_CR57","doi-asserted-by":"publisher","unstructured":"Zhang J, Tang J, Liang B et al (2008) Recommendation Over a Heterogeneous Social Network. In: The Ninth International Conference on Web-age Information Management, WAIM 2008, July 20-22, 2008, Zhangjiajie, China. IEEE Computer Society, pp 309\u2013316, https:\/\/doi.org\/10.1109\/WAIM.2008.71","DOI":"10.1109\/WAIM.2008.71"},{"key":"5113_CR58","unstructured":"Zhang M, Chen Y (2020) Inductive Matrix Completion based on Graph Neural Networks. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, https:\/\/openreview.net\/forum?id=ByxxgCEYDS"},{"issue":"1","key":"5113_CR59","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s41060-016-0031-0","volume":"3","author":"J Zheng","year":"2017","unstructured":"Zheng J, Liu J, Shi C et al (2017) Recommendation in heterogeneous information network via dual similarity regularization. Int J Data Sci Anal 3(1):35\u201348. https:\/\/doi.org\/10.1007\/s41060-016-0031-0","journal-title":"Int J Data Sci Anal"},{"key":"5113_CR60","unstructured":"Zhou S, Bu J, Wang X et al (2019) HAHE: hierarchical attentive heterogeneous information network embedding. arXiv:abs\/1902.01475"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05113-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05113-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05113-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T07:12:35Z","timestamp":1686294755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05113-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":60,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["5113"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05113-6","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2023,3,4]]},"assertion":[{"value":"11 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Written informed consent for publication of this paper was obtained from all authors","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and Consent to participate"}},{"value":"Written informed consent was obtained from the patient for publication of this case report and any accompanying images.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Study does not involve animal or human subjects.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal rights"}}]}}