论文标题
dotmat：解决推荐系统的冷启动问题和减轻稀疏问题
DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems
论文作者
论文摘要
冷启动和稀疏问题是推荐系统的两个关键固有问题。在过去的二十年中，研究人员和工业从业人员已经花费了大量的努力来解决问题。但是，对于冷启动问题，大多数研究都依靠进口侧信息来转移知识。 Zeromat是一个值得注意的例外，它不使用额外的输入数据。稀疏性是一个较少引起的问题。在本文中，我们提出了一种名为dotmat的新算法，该算法依赖于不额外的输入数据，但能够解决冷启动和稀疏问题。在实验中，我们证明像Zeromat一样，DotMat可以通过具有完整数据（例如经典矩阵分解算法）的推荐系统获得竞争结果。
Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge. A notable exception is ZeroMat, which uses no extra input data. Sparsity is a lesser noticed problem. In this paper, we propose a new algorithm named DotMat that relies on no extra input data, but is capable of solving cold-start and sparsity problems. In experiments, we prove that like ZeroMat, DotMat can achieve competitive results with recommender systems with full data, such as the classic matrix factorization algorithm.
