Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences
Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences
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DOI：
10.1109/icde48307.2020.00022
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Kazutoshi Umemoto;Tova Milo;M. Kitsuregawa
中科院分区：
文献类型：
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作者：
Kazutoshi Umemoto;Tova Milo;M. Kitsuregawa
推荐系统如何帮助人们提高技能？作为第一步的建议，提高用户的技能，本文解决了建模的问题，提高用户的技能和项目的难度，在动作序列中，用户在不同的时间选择项目。我们提出了一个渐进模型，使用潜变量来学习用户技能的单调非递减进展。一旦使用给定的序列数据训练了这个模型，我们就可以利用它来找到项目难度估计问题的统计解决方案，我们假设用户通常在他们的技能能力范围内选择项目。在五个数据集上的实验（四个来自真实的领域，一个是合成生成的）表明：（1）我们的模型成功地捕获了领域相关技能的进展;（2）多方面的项目特征有助于学习更好的模型，这些模型与合成数据集中的地面真实技能和难度水平保持一致;（3）学习的模型实际上对预测动作序列中的项目和评级很有用;（4）利用技能模型的依赖结构进行并行计算，提高了训练过程的效率。
How can recommender systems help people improve their skills? As a first step toward recommendation for the upskilling of users, this paper addresses the problems of modeling the improvement of user skills and the difficulty of items in action sequences where users select items at different times. We propose a progression model that uses latent variables to learn the monotonically non-decreasing progression of user skills. Once this model is trained with the given sequence data, we leverage it to find a statistical solution to the item difficulty estimation problem, where we assume that users usually select items within their skill capacity. Experiments on five datasets (four from real domains, and one generated synthetically) revealed that (1) our model successfully captured the progression of domain-dependent skills; (2) multi-faceted item features helped to learn better models that aligned well with the ground-truth skill and difficulty levels in the synthetic dataset; (3) the learned models were practically useful to predict items and ratings in action sequences; and (4) exploiting the dependency structure of our skill model for parallel computation made the training process more efficient.