{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:55:14Z","timestamp":1772931314385,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction. However, standard FMs only produce a single fixed representation for each feature across different input instances, which may limit the CTR model\u2019s expressive and predictive power. Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model. Comprehensive experiments on two real-world CTR prediction datasets show that the DIFM model can outperform several state-of-the-art models consistently.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/434","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"3139-3145","source":"Crossref","is-referenced-by-count":61,"title":["A Dual Input-aware Factorization Machine for CTR Prediction"],"prefix":"10.24963","author":[{"given":"Wantong","family":"Lu","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University"}]},{"given":"Yantao","family":"Yu","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University"}]},{"given":"Yongzhe","family":"Chang","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University"},{"name":"Data 61 CSIRO, Sydney, Australia"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"UBTECH Sydney AI Centre, The University of Sydney, Australia"}]},{"given":"Chenhui","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University"}]},{"given":"Bo","family":"Yuan","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:15:08Z","timestamp":1594246508000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/434"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/434","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}