ABSTRACT

To address the sparsity problem, a novel collaborative filtering approach based on multiple attribute decision making (MADM-CF) is proposed. In MADM-CF, users in collaborative filtering are treated as decision alternatives, items are treated as attributes. The weight of each item is determined, and the preference similarities between the active user and other users are computed. The preference similarity means that how the users’ preferences are similar on positive ratings and negative ratings. According to the preference similarities, the candidate neighbourhood of the active user is determined. A method to compute overall assessment value is designed, the overall assessment value of each user in the candidate neighbourhood is computed, and users with the smallest overall assessment values are selected as the active user’s nearest neighbours. Finally, the most frequent item recommendation method (MFIR) is used to provide top-N recommendations to the active user. Experimental results based on MovieLens and Netflix datasets show that the proposed approach is superior to existing alternatives.

摘要

为了解决稀疏问题，提出了一种基于多属性决策（MADM-CF）的新型协同过滤方法。在 MADM-CF 中，协同过滤中的用户被视为决策选择，项目被视为属性。确定每个项目的权重，并计算活动用户与其他用户之间的偏好相似度。偏好相似度是指用户的偏好在正面评价和负面评价上的相似程度。根据偏好相似度，确定活跃用户的候选邻域。设计了一种综合评价值的计算方法，计算候选邻域内每个用户的综合评价值，选择综合评价值最小的用户作为活跃用户的最近邻。N推荐给活跃用户。基于 MovieLens 和 Netflix 数据集的实验结果表明，所提出的方法优于现有的替代方法。