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Designing recommender systems using deep neural networks (DNNs) requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model\u2013hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multimodality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space\u2019s scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rate (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2\u00d7 floating-point operations efficiency, 1.8\u00d7 energy efficiency, and 1.5\u00d7 performance improvements in recommender models.<\/jats:p>","DOI":"10.1145\/3706124","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T10:57:07Z","timestamp":1733741827000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Automated Model Design on Recommender Systems"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9590-9433","authenticated-orcid":false,"given":"Tunhou","family":"Zhang","sequence":"first","affiliation":[{"name":"Duke University, Durham, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6177-9834","authenticated-orcid":false,"given":"Dehua","family":"Cheng","sequence":"additional","affiliation":[{"name":"Meta Platforms Inc, Menlo Park, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-6552","authenticated-orcid":false,"given":"Yuchen","family":"He","sequence":"additional","affiliation":[{"name":"Meta Platforms Inc, Menlo Park, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5030-3005","authenticated-orcid":false,"given":"Zhengxing","family":"Chen","sequence":"additional","affiliation":[{"name":"Meta Platforms Inc, Menlo Park, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-2714","authenticated-orcid":false,"given":"Xiaoliang","family":"Dai","sequence":"additional","affiliation":[{"name":"Meta Platforms Inc, Menlo Park, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4224-5797","authenticated-orcid":false,"given":"Liang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Meta Platforms Inc, Menlo Park, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9878-3745","authenticated-orcid":false,"given":"Yudong","family":"Liu","sequence":"additional","affiliation":[{"name":"Duke University, Durham, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5936-0030","authenticated-orcid":false,"given":"Feng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Duke University, Durham, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7747-5815","authenticated-orcid":false,"given":"Yufan","family":"Cao","sequence":"additional","affiliation":[{"name":"Duke University, Durham, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9840-7754","authenticated-orcid":false,"given":"Feng","family":"Yan","sequence":"additional","affiliation":[{"name":"University of Houston, Houston, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3228-6544","authenticated-orcid":false,"given":"Hai","family":"Li","sequence":"additional","affiliation":[{"name":"Duke University, Durham, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1486-8412","authenticated-orcid":false,"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[{"name":"Duke University, Durham, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0027-4821","authenticated-orcid":false,"given":"Wei","family":"Wen","sequence":"additional","affiliation":[{"name":"Meta Platforms Inc, Menlo Park, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Layer normalization","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. 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