{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T12:40:01Z","timestamp":1758976801484,"version":"3.44.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4t Hildesheim"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In the context of recommendation systems, addressing the multi-behavior recommendation problem has become increasingly vital for online platforms seeking to comprehend users\u2019 dynamic behavior over time. Recent models employ various techniques, such as graph neural networks and attention mechanisms, to learn users\u2019 behaviors. However, similar to the sequential recommendation, preserving sequential dynamic patterns in multi-behavior recommendation remains crucial. Existing models encounter challenges in learning deep behavioral representations within a given sequence while simultaneously maintaining the temporal order of items. To address this issue, we introduce a novel inter- and intra-attention mechanism for multi-behavior recommendation (IIARec). Specifically, our approach applies intra-self-attention to items of the same behavior, followed by inter-self-attention across all behaviors. Additionally, we propose historical behavior indicators to encode the historical frequency of each item\u2019s behavior in the input sequence. Furthermore, the IIARec model operates in a multitask setting, allowing it to learn item behaviors and their associated scores concurrently. Extensive experimental results on four real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods, illustrating its efficiency in addressing the multi-behavior recommendation challenge.<\/jats:p>","DOI":"10.1007\/s41060-025-00839-3","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:21:39Z","timestamp":1751415699000},"page":"6555-6566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An inter- and intra-attention model for multi-behavior recommendation"],"prefix":"10.1007","volume":"20","author":[{"given":"Shereen","family":"Elsayed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Rashed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Schmidt-Thieme","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"unstructured":"Bachlechner, T., Majumder, B.P., Mao, H., Cottrell, G., McAuley, J.: Rezero is all you need: Fast convergence at large depth. 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