Collaborative filtering over the years have emerged as an alternative recommender system to address some of the setbacks of content based filtering. Although, Collaborative filtering has offered some benefits to the majority of the online stores in recommending products to users using users’ ratings of similarity measure, its usage has also raised some doubt in the minds of researchers, regarding its effectiveness in handling ratings with limited number of users or no rating record from users. Thus, this has resulted in efforts by researchers in determining further ways to combat the issues attributed with the existing collaborative filtering techniques in terms of data sparsity or cold-start situations. This study focused on improving the traditional similarity measurements that currently exist on the item-based collaborative filtering, in order to accommodate and mitigate further the issue of cold-start situations. Thus, this study proposed an algorithm which is meant to balance the three current traditional measurement metrics such as: Cosine-based similarity, Pearson correlation similarity and Adjusted cosine similarity, in the direction of cold-start situations. The improved algorithm of traditional measurement metrics were further compared with the existing algorithm of the traditional metrics. Results showed that the proposed algorithm offered a better item-based collaborative filtering algorithm to recommendation systems than the existing, using data set from Movielens recommender system. Hence, the proposed algorithm did not only mitigate the drawbacks experienced with the three traditional algorithms in terms of data sparsity or cold-start situations but also retained the good features of the existing item-based collaborative filtering algorithm. Thus, the proposed algorithm complemented the strength of the three traditional measurement metrics with evidence shown when measured with Mean Absolute Error.

多年来，协作筛选已成为替代推荐系统，以解决基于内容的筛选的一些挫折。尽管协作过滤为大多数在线商店提供了使用用户的相似性评分向用户推荐产品的某些好处，但是对于其在处理数量有限的评分方面的有效性，其使用也引起了研究人员的怀疑。的用户数或没有用户的评分记录。因此，这导致研究人员努力确定进一步的方法来解决数据稀疏或冷启动情况下现有协作过滤技术引起的问题。这项研究的重点是改进基于项目的协作过滤当前存在的传统相似性度量，为了适应和减轻冷启动情况的问题。因此，本研究提出了一种算法，该算法旨在在冷启动情况下平衡三个当前的传统测量指标，例如：基于余弦的相似度，Pearson相关相似度和调整后的余弦相似度。将改进后的传统度量标准算法与现有传统度量标准算法进行了比较。结果表明，利用Movielens推荐器系统中的数据集，所提出的算法为推荐系统提供了比现有更好的基于项目的协同过滤算法。因此，该算法不仅减轻了三种传统算法在数据稀疏或冷启动情况下的弊端，而且还保留了现有基于项目的协同过滤算法的良好功能。因此，所提出的算法补充了三个传统测量指标的强度，并在用平均绝对误差进行测量时显示了证据。