{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:06:44Z","timestamp":1777705604600,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task.<\/jats:p>","DOI":"10.3233\/jifs-211155","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T19:17:29Z","timestamp":1633634249000},"page":"1679-1691","source":"Crossref","is-referenced-by-count":1,"title":["Learning intents behind interactions with high-order graph for session-based intelligent recommendation"],"prefix":"10.1177","volume":"42","author":[{"given":"Jianfeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruomei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Communication Content Cognition, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-211155_ref1","doi-asserted-by":"crossref","first-page":"776","DOI":"10.2991\/ijcis.2017.10.1.52","article-title":"Fuzzy tools in recommender systems: A survey","volume":"10","author":"Toledo","year":"2017","journal-title":"Int J Comput Intell Syst"},{"key":"10.3233\/JIFS-211155_ref2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl Based Syst"},{"key":"10.3233\/JIFS-211155_ref3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","article-title":"Recommender system application developments: A survey","volume":"74","author":"Lu","year":"2015","journal-title":"Decis Support Syst"},{"key":"10.3233\/JIFS-211155_ref4","first-page":"555","article-title":"Session-based social recommendation via dynamic graph attention networks","author":"Song","year":"2019","journal-title":"ICDM"},{"key":"10.3233\/JIFS-211155_ref5","first-page":"565","article-title":"Personalized top-n sequential recommendation via convolutional sequence embedding","author":"Tang","year":"2018","journal-title":"ICDM"},{"issue":"5","key":"10.3233\/JIFS-211155_ref6","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1177\/0165551515603321","article-title":"Profiling users with tag networks in diffusion-based personalized recommendation","volume":"42","author":"Mao","year":"2016","journal-title":"J Inf Sci"},{"key":"10.3233\/JIFS-211155_ref7","first-page":"3940","article-title":"Graph contextualized self-attention network for session-based recommendation","author":"Xu","year":"2019","journal-title":"IJCAI"},{"key":"10.3233\/JIFS-211155_ref8","first-page":"579","article-title":"Rethinking the item order in session-based recommendation with graph neural networks","author":"Qiu","year":"2019","journal-title":"CIKM"},{"key":"10.3233\/JIFS-211155_ref9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.neucom.2019.09.016","article-title":"Position-aware context attention for session-based recommendation","volume":"376","author":"Cao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-211155_ref10","first-page":"825","article-title":"Hierarchical gating networks for sequential recommendation","author":"Ma","year":"2019","journal-title":"SIGKDD"},{"key":"10.3233\/JIFS-211155_ref11","first-page":"197","article-title":"Self-attentive sequential recommendation","author":"Kang","year":"2018","journal-title":"ICDM"},{"key":"10.3233\/JIFS-211155_ref12","doi-asserted-by":"crossref","unstructured":"Song K. , Ji M. , Park S. and Moon Il-C. , Hierarchical context enabled recurrent neural network for recommendation. In AAAI, pages 4983\u20134991, 2019.","DOI":"10.1609\/aaai.v33i01.33014983"},{"key":"10.3233\/JIFS-211155_ref13","doi-asserted-by":"crossref","unstructured":"Ying R. , He R. , Chen K. , Eksombatchai P. , Hamilton W.L. and Leskovec J. , Graph convolutional neural networks for web-scale recommender systems. In SIGKDD, pages 974\u2013983, 2018.","DOI":"10.1145\/3219819.3219890"},{"key":"10.3233\/JIFS-211155_ref14","doi-asserted-by":"crossref","unstructured":"Quadrana M. , Cremonesi P. and Jannach D. , Sequence-aware recommender systems. In UMAP, pages 373\u2013374, 2018.","DOI":"10.1145\/3209219.3209270"},{"key":"10.3233\/JIFS-211155_ref15","doi-asserted-by":"crossref","unstructured":"He R. and McAuley J.J. , Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM, pages 191\u2013200, 2016.","DOI":"10.1109\/ICDM.2016.0030"},{"key":"10.3233\/JIFS-211155_ref16","first-page":"346","article-title":"Session-based recommendation with graph neural networks","author":"Wu","journal-title":"AAAI"},{"key":"10.3233\/JIFS-211155_ref17","doi-asserted-by":"crossref","unstructured":"Yuan F. , He X. , Karatzoglou A. and Zhang L. , Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. In SIGIR pages 1469\u20131478, 2020.","DOI":"10.1145\/3397271.3401156"},{"key":"10.3233\/JIFS-211155_ref18","doi-asserted-by":"crossref","unstructured":"Rendle S. , Freudenthaler C. and Schmidt-Thieme L. , Factorizing personalized markov chains for nextbasket recommendation. In WWW, pages 811\u2013820, 2010.","DOI":"10.1145\/1772690.1772773"},{"key":"10.3233\/JIFS-211155_ref19","unstructured":"Wang S. , Cao L. and Wang Y. , A survey on session-based recommender systems, CoRR, abs\/1902.04864, 2019."},{"key":"10.3233\/JIFS-211155_ref20","doi-asserted-by":"crossref","unstructured":"Liu S. and Zheng Y. , Long-tail session-based recommendation. In RecSys, pages 509\u2013514, 2020.","DOI":"10.1145\/3383313.3412222"},{"key":"10.3233\/JIFS-211155_ref21","doi-asserted-by":"crossref","unstructured":"Sheu H.-S. and Li S. , Context-aware graph embedding for session-based news recommendation. In RecSys, pages 657\u2013662, 2020.","DOI":"10.1145\/3383313.3418477"},{"key":"10.3233\/JIFS-211155_ref22","doi-asserted-by":"crossref","unstructured":"Jin B. , Gao C. , He X. , Jin D. and Li Y. , Multi-behavior recommendation with graph convolutional networks. In SIGIR, pages 659\u2013668, 2020.","DOI":"10.1145\/3397271.3401072"},{"key":"10.3233\/JIFS-211155_ref23","doi-asserted-by":"crossref","unstructured":"Beutel A. , Covington P. , Jain S. , Xu C. , Li J. , Gatto V. and Chi E.H. , Latent cross: Making use of context in recurrent recommender systems. In WSDM, pages 46\u201354, 2018.","DOI":"10.1145\/3159652.3159727"},{"key":"10.3233\/JIFS-211155_ref24","doi-asserted-by":"crossref","unstructured":"Yuan F. , He X. , Jiang H. , Guo G. , Xiong J. , Xu Z. and Xiong Y. , Future data helps training: Modeling future contexts for session-based recommendation. In WWW, pages 303\u2013313, 2020.","DOI":"10.1145\/3366423.3380116"},{"key":"10.3233\/JIFS-211155_ref25","doi-asserted-by":"crossref","unstructured":"Quadrana M. , Karatzoglou A. , Hidasi B. and Cremonesi P. , Personalizing session-based recommendations with hierarchical recurrent neural networks. In RecSys, pages 130\u2013137, 2017.","DOI":"10.1145\/3109859.3109896"},{"key":"10.3233\/JIFS-211155_ref26","doi-asserted-by":"crossref","unstructured":"Zheng Y. , Liu S. , Li Z. and Wu S. , DGTN: dualchannel graph transition network for session-based recommendation. pages 236\u2013242, 2020.","DOI":"10.1109\/ICDMW51313.2020.00041"},{"key":"10.3233\/JIFS-211155_ref27","doi-asserted-by":"crossref","unstructured":"Abugabah A. , Xiaochun and WangJ., Dynamic graph attention-aware networks for session-based recommendation. In IJCNN, pages 1\u20137, 2020.","DOI":"10.1109\/IJCNN48605.2020.9206914"},{"key":"10.3233\/JIFS-211155_ref28","doi-asserted-by":"crossref","unstructured":"Pan Z. , Cai F. , Ling Y. and de RijkeM., Rethinking item importance in session-based recommendation. In SIGIR, pages 1837\u20131840, 2020.","DOI":"10.1145\/3397271.3401274"},{"key":"10.3233\/JIFS-211155_ref29","doi-asserted-by":"crossref","unstructured":"Lv F. , Jin T. , Yu C. , Sun F. , Lin Q. , Yang K. and Ng W. , SDM: sequential deep matching model for online large-scale recommender system. In CIKM, pages 2635\u20132643, 2019.","DOI":"10.1145\/3357384.3357818"},{"issue":"9","key":"10.3233\/JIFS-211155_ref30","doi-asserted-by":"crossref","first-page":"902","DOI":"10.3390\/e21090902","article-title":"Optimization of big data scheduling in social networks","volume":"21","author":"Fu","year":"2019","journal-title":"Entropy"},{"key":"10.3233\/JIFS-211155_ref31","doi-asserted-by":"crossref","unstructured":"Liang T. , Li Y. , Li R. , Gu X. , Habimana O. and Hu Y. , Personalizing session-based recommendation with dual attentive neural network. In IJCNN, pages 1\u20138, 2019.","DOI":"10.1109\/IJCNN.2019.8852185"},{"key":"10.3233\/JIFS-211155_ref32","doi-asserted-by":"crossref","unstructured":"Wang Z. , Wei W. , Cong G. , Li X.-L. , Mao X. and Qiu M. , Global context enhanced graph neural networks for session-based recommendation. In SIGIR, pages 169\u2013178, 2020.","DOI":"10.1145\/3397271.3401142"},{"key":"10.3233\/JIFS-211155_ref33","doi-asserted-by":"crossref","unstructured":"Lee J.B. , Rossi R.A. , Kong X. , Kim S. , Koh E. and Rao A. , Graph convolutional networks with motif-based attention. In CIKM, pages 499\u2013508, 2019.","DOI":"10.1145\/3357384.3357880"},{"key":"10.3233\/JIFS-211155_ref34","doi-asserted-by":"crossref","unstructured":"Wang J. , Xu Q. , Lei J. , Lin C. and Xiao B. , PA-GGAN: session-based recommendation with position-aware gated graph attention network. In ICME, pages 1\u20136, 2020.","DOI":"10.1109\/ICME46284.2020.9102758"},{"key":"10.3233\/JIFS-211155_ref35","doi-asserted-by":"crossref","unstructured":"Li J. , Ren P. , Chen Z. , Ren Z. , Lian T. and Ma J. , Neural attentive session-based recommendation. In CIKM, pages 1419\u20131428, 2017.","DOI":"10.1145\/3132847.3132926"},{"key":"10.3233\/JIFS-211155_ref36","doi-asserted-by":"crossref","unstructured":"Chen T. and Wong R.C.-W. , Handling information loss of graph neural networks for session-based recommendation. In SIGKDD, pages 1172\u20131180, 2020.","DOI":"10.1145\/3394486.3403170"},{"key":"10.3233\/JIFS-211155_ref37","doi-asserted-by":"crossref","unstructured":"Guo L. , Yin H. , Wang Q. , Chen T. , Zhou A. and Hung N.Q.V. , Streaming session-based recommendation. In SIGKDD, pages 1569\u20131577, 2019.","DOI":"10.1145\/3292500.3330839"},{"issue":"6","key":"10.3233\/JIFS-211155_ref38","doi-asserted-by":"crossref","first-page":"102099","DOI":"10.1016\/j.ipm.2019.102099","article-title":"Dynamic attention-based explainable recommendation with textual and visual fusion","volume":"57","author":"Liu","year":"2020","journal-title":"Inf Process Manag"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:24Z","timestamp":1777455864000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,2]]},"references-count":38,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211155","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,2]]}}}