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Firstly, multi-source heterogeneous auxiliary information knowledge is fused to supplement semantics of program and user to obtain initial representations that contain rich semantics, then user and program node embedding representations are aggregated in multiple layers through graph neural networks to model higher-order interaction history information and realize user and program representation update; finally, user viewing prediction is performed based on deep networks to realize personalized program recommendation. The final experiment results in indicators, such as normalized discounted cumulative gain (NDCG), hit rate (HR) and root mean square error (RMSE), verified the effectiveness of this method by comparing with various methods.<\/jats:p>","DOI":"10.1007\/s40747-022-00645-5","type":"journal-article","created":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T13:11:09Z","timestamp":1643029869000},"page":"2311-2324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Enhanced graph recommendation with heterogeneous auxiliary information"],"prefix":"10.1007","volume":"8","author":[{"given":"Fulian","family":"Yin","sequence":"first","affiliation":[]},{"given":"Meiqi","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Yanyan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zebin","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Feng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7531-400X","authenticated-orcid":false,"given":"Sitong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"645_CR1","doi-asserted-by":"publisher","first-page":"886","DOI":"10.26599\/TST.2020.9010051","volume":"26","author":"X Ao","year":"2021","unstructured":"Ao X, Derong L, Hongkang T, Zhengyuan L, Peng Y, Michel K (2021) News keyword extraction algorithm based on semantic clustering and word graph model. 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