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Recomm. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            News recommendation aims to offer potentially interesting news items to a specific user, guided by his historical browsing behaviors. Existing methods failed to effectively address the knowledge sparsity issue that the user may have sparse behaviors and the news may own sparse features. To address the problem, we propose a graph-based news recommendation model with intrinsic interest enhancement, named\n            <jats:bold>GIIE<\/jats:bold>\n            , leveraging intrinsic interests and neighbor information to enhance the representation of sparse users and news. Concretely, to fully take advantage of the intrinsic interests, we design an interest encoder based on an interest-type graph with a learnable structure and explore the interest embeddings from news types. Then, we inject the obtained interest embeddings into news and represent the user by aggregating the clicked news under the same interest and across different interests sequentially. These interests can build a bridge between users so users with sparse behaviors can implicitly share knowledge with other users, thereby enhancing their representation. To properly introduce the neighbor knowledge, we propose a graph-based neighbor enhancing mechanism. First, we design a news relation graph and a user relation graph in encoders. Then, based on these graphs, we take the attention module to aggregate additional knowledge from neighbors, enhancing sparse news and user representations. To avoid feature ambiguity, we adopt a way to represent the current item (user and news) and its neighbors separately and then do adaptive aggregation. We evaluate GIIE on the public news recommendation datasets MIND-Large and MIND-Small. Experimental results show that our model can solve the knowledge-sparse problem and outperforms current state-of-the-art models in four indicators.\n          <\/jats:p>","DOI":"10.1145\/3654803","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T18:54:21Z","timestamp":1726599261000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["GIIE: A Graph-based News Recommendation Model with Intrinsic Interest Enhancement"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-9932","authenticated-orcid":false,"given":"Yiming","family":"Hei","sequence":"first","affiliation":[{"name":"China Academy of Information and Communications Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2965-3518","authenticated-orcid":false,"given":"Jianwei","family":"Liu","sequence":"additional","affiliation":[{"name":"BUAA, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5536-9276","authenticated-orcid":false,"given":"Shicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering CAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0179-2364","authenticated-orcid":false,"given":"Lihong","family":"Wang","sequence":"additional","affiliation":[{"name":"National Computer Network Emergency Response Technical Team Coordination Center of China, Beijing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8952-8984","authenticated-orcid":false,"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8266-5353","authenticated-orcid":false,"given":"Yong","family":"Yang","sequence":"additional","affiliation":[{"name":"State Grid Handan Electric Power Supply Company, Handan China"}]}],"member":"320","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1033"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1033"},{"key":"e_1_3_2_4_2","article-title":"Multi-task item-attribute graph pre-training for strict cold-start item recommendation","volume":"2306","author":"Cao Yuwei","year":"2023","unstructured":"Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, and Philip S. 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