User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is a useful approach that we have proposed in our previous work, which greatly improves the traditional UBCF and could provide satisfactory recommendations to an active user which often has sufficient rating information. However, different from an active user, a new user in RSs often has special characteristics (e.g., fewer ratings or ratings concentrating on popular items), and the previous CBCF approach cannot provide satisfactory recommendations for a new user. In this paper, aiming to provide personalized recommendations for a new user, through a detailed analysis of the characteristics of new users, we reconstruct a decision class to improve the previous CBCF and utilize the covering reduction algorithm in covering-based rough sets to remove redundant candidate neighbors for a new user. Furthermore, unlike the previous CBCF, our improved CBCF could provide personalized recommendations without needing special additional information. Experimental results suggest that for the sparse datasets that often occur in real RSs, the improved CBCF significantly outperforms those of existing work and can provide personalized recommendations for a new user with satisfactory accuracy and diversity simultaneously.

中文翻译：

---

改进的基于覆盖的协作过滤，针对新用户的个性化推荐

基于用户的协作过滤（UBCF）作为最成功的方法之一在推荐系统（RS）中得到广泛使用，但是传统的UBCF无法同时提供具有令人满意的准确性和多样性的推荐。基于覆盖的协作过滤（CBCF）是我们在之前的工作中提出的一种有用的方法，它大大改进了传统的UBCF，并且可以为经常有足够评级信息的活跃用户提供令人满意的建议。但是，与活跃用户不同，RS中的新用户通常具有特殊的特征（例如，较少的收视率或关注流行项目的收视率），并且以前的CBCF方法无法为新用户提供令人满意的推荐。本文旨在为新用户提供个性化推荐，通过对新用户特征的详细分析，我们重构了一个决策类以改进先前的CBCF，并在基于覆盖的粗糙集中利用覆盖减少算法来为新用户去除多余的候选邻居。此外，与以前的CBCF不同，我们改进后的CBCF可以提供个性化建议，而无需特殊的附加信息。实验结果表明，对于在真实RS中经常出现的稀疏数据集，改进后的CBCF明显优于现有工作，并且可以同时为新用户提供令人满意的准确性和多样性的个性化推荐。我们重构了一个决策类以改进以前的CBCF，并在基于覆盖的粗糙集中利用覆盖减少算法来为新用户删除多余的候选邻居。此外，与以前的CBCF不同，我们改进后的CBCF可以提供个性化建议，而无需特殊的附加信息。实验结果表明，对于在真实RS中经常出现的稀疏数据集，改进后的CBCF明显优于现有工作，并且可以同时为新用户提供令人满意的准确性和多样性的个性化推荐。我们重构了一个决策类以改进以前的CBCF，并在基于覆盖的粗糙集中利用覆盖减少算法来为新用户删除多余的候选邻居。此外，与以前的CBCF不同，我们改进后的CBCF可以提供个性化建议，而无需特殊的附加信息。实验结果表明，对于在真实RS中经常出现的稀疏数据集，改进后的CBCF明显优于现有工作，并且可以同时为新用户提供令人满意的准确性和多样性的个性化推荐。