{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T22:52:37Z","timestamp":1754261557089},"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>In large-scale e-commerce live-stream recommendation, streamers are classified into different levels based on their popularity and other metrics for marketing. Several top streamers at the head level occupy a considerable amount of exposure, resulting in an unbalanced data distribution. A unified model for all levels without consideration of imbalance issue can be biased towards head streamers and neglect the conflicts between levels. The lack of inter-level streamer correlations and intra-level streamer characteristics modeling imposes obstacles to estimating the user behaviors. To tackle these challenges, we propose a curriculum multi-level learning framework for imbalanced recommendation. We separate model parameters into shared and level-specific ones to explore the generality among all levels and discrepancy for each level respectively. The level-aware gradient descent and a curriculum sampling scheduler are designed to capture the de-biased commonalities from all levels as the shared parameters. During the specific parameters training, the hardness-aware learning rate and an adaptor are proposed to dynamically balance the training process. Finally, shared and specific parameters are combined to be the final model weights and learned in a cooperative training framework. Extensive experiments on a live-stream production dataset demonstrate the superiority of the proposed framework.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/267","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"2406-2414","source":"Crossref","is-referenced-by-count":2,"title":["Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation"],"prefix":"10.24963","author":[{"given":"Shuodian","family":"Yu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Junqi","family":"Jin","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Li","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Xiaofeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Xiaopeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Haiyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Jian","family":"Xu","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","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":"2023-08-11T08:43:27Z","timestamp":1691743407000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/267"}},"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\/267","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}