Practically Feasible Recommender Systems for Cold Start Problems
Practically Feasible Recommender Systems for Cold Start Problems
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DOI：
10.1109/apwconcse.2018.00025
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Mimu Kawai;Takayuki Shiohama;Hiroyuki Sato
中科院分区：
文献类型：
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作者：
Mimu Kawai;Takayuki Shiohama;Hiroyuki Sato
推荐系统对服务提供商和用户都是有益的，因为它们基于个人用户的偏好向个人用户提供项目推荐。然而，推荐系统存在冷启动问题，这是指很难为新添加的用户推荐新项目的事实。针对现有用户和项目的推荐系统已经被大力研究。另一方面，冷启动问题没有得到足够的重视。在本文中，我们提出了推荐过滤的方法，可以应用于解决冷启动问题。我们的模型提供了简单的矩阵近似方法，用于分析来自潜在项目的大量未标记数据。我们使用基于伪逆矩阵的矩阵分解技术进行评级近似，这使我们能够通过使用用户特征矩阵，项目特征矩阵和评级矩阵来进行冷启动推荐。所提出的模型可以显式地学习用户人口统计和项目特征，以克服冷启动问题。数值实验的结果表明，当在MovieLens 1M数据集上进行测试时，所提出的方法优于几个基线。
Recommender systems are beneficial to both service providers and users, as they offer item recommendations to individual users based on their preferences. However, the recommender systems present cold start problems, which refer to the fact that it is difficult to recommend new items for newly added users. Recommender systems for existing users and items have been researched vigorously. On the other hand, cold start problems have received insufficient attention. In this paper, we propose methods for recommender filtering that can be applied to address cold start problems. Our models provide simple matrix approximation methods for analyzing large volumes of unlabeled data from potential items. We use matrix factorization techniques based on a pseudo-inverse matrix for rating approximation, which enables us to make cold start recommendations by using a user–characteristic matrix, item–feature matrix, and rating matrix. The proposed models can explicitly learn user demographics and item features to overcome the cold start problems. The results of a numerical experiment show that the proposed methods outperform several baselines when tested on the MovieLens 1M dataset.