{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:27:59Z","timestamp":1742912879838,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031714634"},{"type":"electronic","value":"9783031714641"}],"license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-71464-1_29","type":"book-chapter","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T17:51:25Z","timestamp":1731433885000},"page":"347-359","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["REHG: A Recommender Engine Based on Heterogeneous Graph"],"prefix":"10.1007","author":[{"given":"Xiaoyang","family":"Xin","sequence":"first","affiliation":[]},{"given":"Zesheng","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Tiankuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mengqiu","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Ruixuan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Chong","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Jianbo","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","volume":"74","author":"J Lu","year":"2015","unstructured":"Lu, J., et al.: Recommender system application developments: a survey. Decis. Support. Syst. 74, 12\u201332 (2015)","journal-title":"Decis. Support. Syst."},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: 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":"29_CR3","doi-asserted-by":"crossref","unstructured":"He, X., et al.: 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":"29_CR4","doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: How powerful is graph convolution for recommendation? In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (2021)","DOI":"10.1145\/3459637.3482264"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Choi, J., et al.: Blurring-sharpening process models for collaborative filtering. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (2023)","DOI":"10.1145\/3539618.3591645"},{"key":"29_CR6","first-page":"1","volume-title":"Information Retrieval and Mining in Distributed Environments","author":"L Boratto","year":"2011","unstructured":"Boratto, L., Carta, S.: State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information Retrieval and Mining in Distributed Environments, pp. 1\u201320. Springer (2011)"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Liu, F., et al.: Interest-aware message-passing GCN for recommendation. In: Proceedings of the Web Conference 2021 (2021)","DOI":"10.1145\/3442381.3449986"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Recommender Systems Handbook, pp. 1\u201334 (2015)","DOI":"10.1007\/978-1-4899-7637-6_1"},{"key":"29_CR9","unstructured":"Rendle, S., et al.: BPR: bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)"},{"issue":"2","key":"29_CR10","first-page":"1","volume":"38","author":"C Chen","year":"2020","unstructured":"Chen, C., et al.: Efficient neural matrix factorization without sampling for recommendation. ACM Trans. Inform. Syst. (TOIS) 38(2), 1\u201328 (2020)","journal-title":"ACM Trans. Inform. Syst. (TOIS)"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Steck, H.: Embarrassingly shallow autoencoders for sparse data. In: The World Wide Web Conference (2019)","DOI":"10.1145\/3308558.3313710"},{"key":"29_CR12","unstructured":"Hu, J., et al.: MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering. arXiv preprint arXiv:2204.02338 (2022)"},{"issue":"2","key":"29_CR13","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2018","unstructured":"Shi, C., et al.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357\u2013370 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Dong, Y., Chawla, N.V., Swami, A.: Metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)","DOI":"10.1145\/3097983.3098036"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Sun, J., et al.: Multi-graph convolution collaborative filtering. In: 2019 IEEE International Conference on Data Mining (ICDM). IEEE (2019)","DOI":"10.1109\/ICDM.2019.00165"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"29_CR17","unstructured":"Han, Z., et al.: Metapath-and entity-aware graph neural network for recommendation. CoRR, abs\/2010.11793 (2020)"},{"key":"29_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113303","volume":"283","author":"T Yang","year":"2022","unstructured":"Yang, T., Yu, H., Wang, Y.: An efficient low-pass-filtering algorithm to de-noise global GRACE data. Remote Sens. Environ. 283, 113303 (2022)","journal-title":"Remote Sens. Environ."},{"issue":"2","key":"29_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","volume":"2","author":"F Xia","year":"2021","unstructured":"Xia, F., et al.: Graph learning: a survey. IEEE Trans. Artif. Intell. 2(2), 109\u2013127 (2021)","journal-title":"IEEE Trans. Artif. Intell."},{"key":"29_CR20","first-page":"25058","volume":"34","author":"H Chang","year":"2021","unstructured":"Chang, H., et al.: Not all low-pass filters are robust in graph convolutional networks. Adv. Neural. Inf. Process. Syst. 34, 25058\u201325071 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR21","unstructured":"Yu, M., Xue, M., Ouyang, W.: Restaurants Review Star Prediction for Yelp Dataset, Technical Report 17. UCSD (2015)"},{"issue":"4","key":"29_CR22","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. Interact. Intell. Syst. (TIIS) 5(4), 1\u201319 (2015)","journal-title":"ACM Trans. Interact. Intell. Syst. (TIIS)"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191\u2013198 (2016)","DOI":"10.1145\/2959100.2959190"},{"key":"29_CR24","unstructured":"Ma, J., Zhou, C., Cui, P., Yang, H., Zhu, W.: Learning disentangled representations for recommendation. In: NeurIPS, vol. 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Wireless Artificial Intelligent Computing Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-71464-1_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T18:05:59Z","timestamp":1731434759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-71464-1_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"ISBN":["9783031714634","9783031714641"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-71464-1_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,13]]},"assertion":[{"value":"13 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WASA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Artificial Intelligent Computing Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wasa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasa-conference.org\/WASA2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}