{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T14:03:26Z","timestamp":1771682606974,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This study examines how intelligent recommendation algorithms can be used to analyze user purchasebehavior on e-commerce platforms and to support the development of more effective marketing strategies.First, a systematic review of existing recommendation algorithms is conducted, which shows thattraditional methods have limitations when processing large-scale and complex user data. To address theseissues, an Improved Frequent Pattern-growth (IFP-growth) algorithm is introduced. By incorporatingtime-window parameters, the algorithm mines frequent patterns from user behavior logs and extractstimely and relevant purchasing features. In parallel, an Attention-enhanced Deep Factorization Machine(DeepFM) is adopted, integrating the strengths of Factorization Machines and Deep Neural Networks.The attention mechanism further refines feature-interaction representations, enabling more accurate andpersonalized recommendations. To validate the effectiveness and robustness of the proposed model,experiments are conducted using real behavioral data from a major e-commerce platform over a threemonth period, comprising approximately 1.2 million behavior logs, 80,000 active users, and 100,000products. The IFP-DeepFM model is compared with traditional collaborative filtering, IFP-growth, andstandard DeepFM algorithms. The results show that IFP-DeepFM outperforms DeepFM in precision,recall, F1-score, and the area under the precision\u2013recall curve by 6.81%, 11.9%, 9.51%, and 11.5%,respectively. In practical metrics such as Click-Through Rate (CTR) and Conversion Rate (CVR), themodel also achieves improvements of 10.4% and 8.9%. Based on these results, this study outlines severalmarketing strategy implications for e-commerce platforms, including personalized recommendations,targeted advertising, and user behavior prediction. These strategies enhance user satisfaction andplatform performance while providing data-driven support for operational decision-making. Theproposed IFP-DeepFM model demonstrates a practical approach to analyzing user purchase behaviorand refining marketing strategies in large-scale e-commerce settings.<\/jats:p>","DOI":"10.31449\/inf.v50i7.12775","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:15:54Z","timestamp":1771679754000},"source":"Crossref","is-referenced-by-count":0,"title":["IFP-DeepFM: Integrating Improved FP-Growth and Attention-Based Deep Factorization Machines for User Purchase Behavior Modeling and E-commerce Recommendation"],"prefix":"10.31449","volume":"50","author":[{"given":"Ning","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihan","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12775\/6495","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12775\/6495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:15:55Z","timestamp":1771679755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i7.12775","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}