{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T14:21:22Z","timestamp":1790346082807,"version":"4.1.0"},"reference-count":82,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Leveraging the side information associated with entities (i.e., users and items) to enhance recommendation systems has been widely recognized as an essential modeling dimension. Most of the existing approaches address this task by the<jats:italic>integration-based scheme<\/jats:italic>, which incorporates the entity side information by combining the recommendation objective with an extra side information-aware objective. Despite the growing progress made by the existing integration-based approaches, they are largely limited by the potential conflicts between the two objectives. Moreover, the heterogeneous side information among entities is still under-explored in these systems. In this article, we propose a novel<jats:italic>pre-training scheme<\/jats:italic>to leverage the entity side information by pre-training entity embeddings using the multi-graph neural network. Instead of jointly training with two objectives, our<jats:italic>pre-training scheme<\/jats:italic>first pre-trains two representation models under the entity multi\/single relational graphs constructed by their side information and then fine-tunes their embeddings under an existing general representation-based recommendation model. Our proposed multi-graph and single-graph neural networks can generate within-entity knowledge-encapsulated embeddings, while capturing the heterogeneity from the entity side information simultaneously, thereby improving the performance of the underlying recommendation model. An extensive evaluation of our pre-training scheme fine-tuned under four general representation-based recommender models, namely, MF, NCF, NGCF, and LightGCN, shows that effectively pre-training embeddings with both the user\u2019s and item\u2019s side information can significantly improve these original models in terms of both effectiveness and stability.<\/jats:p>","DOI":"10.1145\/3568953","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T13:44:44Z","timestamp":1669988684000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Graph Neural Pre-training for Recommendation with Side Information"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7326-2883","authenticated-orcid":false,"given":"Siwei","family":"Liu","sequence":"first","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5374-0318","authenticated-orcid":false,"given":"Zaiqiao","family":"Meng","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-279X","authenticated-orcid":false,"given":"Craig","family":"Macdonald","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4701-3223","authenticated-orcid":false,"given":"Iadh","family":"Ounis","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. 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