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Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>\n            Knowledge graphs (KGs) can provide users with semantic information and relations among numerous entities and nodes, which can greatly facilitate the performance of recommender systems. However, existing KG-based approaches still suffer from severe data sparsity and may not be effective in capturing the preference features of similar entities across domains. Therefore, in this article, we propose a\n            <jats:bold>P<\/jats:bold>\n            reference-aware\n            <jats:bold>G<\/jats:bold>\n            raph\n            <jats:bold>A<\/jats:bold>\n            ttention network model with\n            <jats:bold>C<\/jats:bold>\n            ollaborative\n            <jats:bold>K<\/jats:bold>\n            nowledge\n            <jats:bold>G<\/jats:bold>\n            raph (\n            <jats:bold>PGACKG<\/jats:bold>\n            ) for cross-domain recommendations. Preference-aware entity embeddings with some collaborative signals are first obtained by exploiting the graph-embedding model, which can transform entities and items in the collaborative knowledge graph into semantic preference spaces. To better learn user preference features, we devise a preference-aware graph attention network framework that aggregates the preference features of similar entities within domains and across domains. In this framework, multi-hop reasoning is employed to assist in the generation of preference features within domains, and the node random walk based on frequency visits is proposed to gather similar preferences across domains for target entities. Then, the final preference features of entities are fused, while a novel\n            <jats:bold>C<\/jats:bold>\n            ross-domain\n            <jats:bold>B<\/jats:bold>\n            ayesian\n            <jats:bold>P<\/jats:bold>\n            ersonalized\n            <jats:bold>R<\/jats:bold>\n            anking (\n            <jats:bold>CBPR<\/jats:bold>\n            ) is proposed to improve cross-domain recommendation accuracy. Extensive empirical experiments on four real-world datasets demonstrate that our proposed approach consistently outperforms state-of-the-art baselines. Furthermore, our PGACKG achieves strong performance in different ablation scenarios, and the interaction sparsity experiments also demonstrate that our proposed approach can significantly alleviate the data sparsity issue.\n          <\/jats:p>","DOI":"10.1145\/3576921","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T13:20:32Z","timestamp":1671196832000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Preference-aware Graph Attention Networks for Cross-Domain Recommendations with Collaborative Knowledge Graph"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8694-3063","authenticated-orcid":false,"given":"Yakun","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8907-3526","authenticated-orcid":false,"given":"Lei","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6244-0664","authenticated-orcid":false,"given":"Juanzi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"issue":"4","key":"e_1_3_2_2_2","first-page":"1","article-title":"Graph Co-Attentive session-based recommendation","volume":"40","author":"Pan Zhiqiang","year":"2021","unstructured":"Zhiqiang Pan, Fei Cai, Wanyu Chen, and Honghui Chen 2021. 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