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However, the heavy deep architectures in existing models are usually incorporated in an embedded manner, which may greatly increase the model complexity and lower the runtime efficiency.<\/jats:p>\n          <jats:p>\n            To simultaneously take the power of deep learning for external knowledge modeling as well as maintaining the model efficiency at test time, we reformulate the problem of recommendation with external knowledge into a\n            <jats:italic>generalized distillation framework<\/jats:italic>\n            . The general idea is to free the complex deep architecture into a separate model, which is only used in the training phrase, while abandoned at test time. In particular, in the training phrase, the external knowledge is processed by a comprehensive teacher model to produce valuable information to teach a simple and efficient student model. Once the framework is learned, the teacher model is abandoned, and only the succinct yet enhanced student model is used to make fast predictions at test time. In this article, we specify the external knowledge as user review, and to leverage it in an effective manner, we further extend the traditional\n            <jats:italic>generalized distillation framework<\/jats:italic>\n            by designing a Selective Distillation Network (SDNet) with\n            <jats:italic>adversarial adaption<\/jats:italic>\n            and\n            <jats:italic>orthogonality constraint<\/jats:italic>\n            strategies to make it more robust to noise information.\n          <\/jats:p>\n          <jats:p>Extensive experiments verify that our model can not only improve the performance of rating prediction, but also can significantly reduce time consumption when making predictions as compared with several state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3281659","type":"journal-article","created":{"date-parts":[[2018,12,13]],"date-time":"2018-12-13T15:45:03Z","timestamp":1544715903000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Adversarial Distillation for Efficient Recommendation with External Knowledge"],"prefix":"10.1145","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0144-1775","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rutgers University, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongteng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of ECE, Duke University, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyuan","family":"Zha","sequence":"additional","affiliation":[{"name":"College of Computing, Georgia Institute of Technology, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_2_1","unstructured":"Konstantinos Bousmalis George Trigeorgis Nathan Silberman Dilip Krishnan and Dumitru Erhan. 2016. 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