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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users\u2019 historical behavior data are usually insufficient. In fact, user\u2019s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users\u2019 behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users\u2019 behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.<\/jats:p>","DOI":"10.1145\/3442201","type":"journal-article","created":{"date-parts":[[2021,4,18]],"date-time":"2021-04-18T16:05:45Z","timestamp":1618761945000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network"],"prefix":"10.1145","volume":"15","author":[{"given":"Yi","family":"Ouyang","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Tang","sequence":"additional","affiliation":[{"name":"Noah\u2019s Ark Lab, Huawei, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuqiang","family":"He","sequence":"additional","affiliation":[{"name":"Noah\u2019s Ark Lab, Huawei, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Xiong","sequence":"additional","affiliation":[{"name":"Tencent, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2013.50"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2nd International Conference on Learning Representations. 1--14","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2014 . 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