Recommender system is adored in the internet industry as one of the most profitable technologies. Unlike other sectors such as fraud detection in the Fintech industry, recommender system is both deep and broad. In recent years, many researchers start to focus on the cold-start problem of recommender systems. In spite of the large volume of research literature, the majority of the research utilizes transfer learning / meta learning and pretrained model to solve the problem. Although the researchers claim the effectiveness of the approaches, everyone of them does rely on extra input data from other sources. In 2021 and 2022, several zeroshot learning algorithm for recommender system such as ZeroMat, DotMat, PoissonMat and PowerMat were invented. They are the first batch of the algorithms that rely on no transfer learning or pretrained models to tackle the problem. In this paper, we follow this line and invent a new zeroshot learning algorithm named LogitMat. We take advantage of the Zipf Law property of the user item rating values and logistic regression model to tackle the cold-start problem and generate competitive results with other competing techniques. We prove in experiments that our algorithm is fast, robust and effective.

翻译：推荐系统作为互联网行业最盈利的技术之一备受推崇。与其他领域（如金融科技行业的欺诈检测）不同，推荐系统兼具深度与广度。近年来，众多研究者开始聚焦推荐系统的冷启动问题。尽管已有大量研究文献，但多数研究采用迁移学习/元学习和预训练模型来解决该问题。尽管研究者声称这些方法的有效性，但每种方法都依赖于来自其他来源的额外输入数据。2021年至2022年间，涌现出如ZeroMat、DotMat、PoissonMat和PowerMat等推荐系统零样本学习算法。这些是首批无需迁移学习或预训练模型即可解决该问题的算法类别。本文沿袭这一思路，提出名为LogitMat的新型零样本学习算法。我们利用用户-物品评分值的齐普夫分布特性与逻辑回归模型处理冷启动问题，并取得了与其他竞争技术相匹敌的结果。实验证明，我们的算法具有快速、鲁棒且高效的特点。