{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T21:01:38Z","timestamp":1773176498561,"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":[[2023,8]]},"abstract":"<jats:p>Transformer-based models are powerful for modeling temporal dynamics of user preference in sequential recommendation. Most of the variants adopt the Softmax transformation in the self-attention layers to generate dense attention probabilities. However, real-world item sequences are often noisy, containing a mixture of true-positive and false-positive interactions. Such dense attentions inevitably assign probability mass to noisy or irrelevant items, leading to sub-optimal performance and poor explainability. Here we propose a Probabilistic Masked Attention Network (PMAN) to identify the sparse pattern of attentions, which is more desirable for pruning noisy items in sequential recommendation. Specifically, we employ a probabilistic mask to achieve sparse attentions under a constrained optimization framework. As such, PMAN allows to select which information is critical to be retained or dropped in a data-driven fashion. Experimental studies on real-world benchmark datasets show that PMAN is able to improve the performance of Transformers significantly.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/230","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"2068-2076","source":"Crossref","is-referenced-by-count":9,"title":["Probabilistic Masked Attention Networks  for Explainable Sequential Recommendation"],"prefix":"10.24963","author":[{"given":"Huiyuan","family":"Chen","sequence":"first","affiliation":[{"name":"Visa Research"}]},{"given":"Kaixiong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Rice University"}]},{"given":"Zhimeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Texas A&M University"}]},{"given":"Chin-Chia Michael","family":"Yeh","sequence":"additional","affiliation":[{"name":"Visa Research"}]},{"given":"Xiaoting","family":"Li","sequence":"additional","affiliation":[{"name":"Visa Research"}]},{"given":"Menghai","family":"Pan","sequence":"additional","affiliation":[{"name":"Visa Research"}]},{"given":"Yan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Visa Research"}]},{"given":"Xia","family":"Hu","sequence":"additional","affiliation":[{"name":"Rice University"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Visa Research"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:42:12Z","timestamp":1691743332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/230"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/230","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}