{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:54:56Z","timestamp":1773856496463,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Online advertising fraud is still a significant problem that inflates marketing expenses and distorts campaign performance. In order to improve fraud detection accuracy in the face of extreme class imbalance, this study proposes a hybrid DeepFM\u2013MOTSO framework that combines a Deep Factorization Machine (DeepFM) with a Multi-Objective Tuna Swarm Optimizer (MOTSO). While MOTSO simultaneously optimizes several goals\u2014maximizing F1-score, precision, recall, and AUC, as well as minimizing loss\u2014for balanced classification, the model captures both low-order feature interactions through the FM component and high-order nonlinear representations via deep neural layers. A real-world advertising dataset with roughly 2,043 records that included contextual and behavioral features like session_duration, click_interval, impression_count, and device_type was used to test the method. With an accuracy of 0.952, precision of 0.214, recall of 0.179, F1-score of 0.195, and AUC of 0.864, the experimental results demonstrate that the proposed DeepFM\u2013MOTSO outperformed all comparative baselines, indicating superior capability in identifying minority-class fraudulent instances. The findings confirm that multi-objective optimization effectively improves model convergence, stability, and real-time adaptability for intelligent online advertising fraud detection.<\/jats:p>","DOI":"10.31449\/inf.v50i10.12769","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:36Z","timestamp":1773832356000},"source":"Crossref","is-referenced-by-count":0,"title":["DeepFM-MOTSO: A Deep Factorization Machine Framework Optimized by Multi-Objective Tuna Swarm for Online Advertising Fraud Detection"],"prefix":"10.31449","volume":"50","author":[{"given":"Dandan","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12769\/6593","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12769\/6593","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:36Z","timestamp":1773832356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.12769","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}