基于用户属性与评分相似因子的推荐算法研究

Abstract

为了使用户接收更准确和更加个性化的推荐信息，改善当前推荐系统因为数据稀疏、冷启动问题带来的诸多影响。本文采取了将用户基本属性、评分时间戳与用户的评分、偏好、评价项目的相似因子相结合的协同过滤算法，提出了基于用户基本属性与评分相似因子相结合的冷启动推荐算法。通过与传统方法在Movie Lens数据集上的对比实验，该方法展现出更好的推荐精度和对数据稀疏情况的良好适应性。 In order to make the user receive more accurate and more personalized recommendation information, this paper improves the influence of the current recommendation system due to the sparse data and the cold start problem. This paper takes the basic attributes of the user, the score timestamp and the user's rating, The similarity factor of the project is combined with the cooperative filtering algorithm, and a cold start recommendation algorithm based on the combination of basic attributes and similarity factors is proposed. This method exhibits better recommendation accuracy and good adaptability to data sparseness by comparing experiments with traditional methods on the Movie Lens dataset.