传统的协同过滤算法存在因评分预测误差导致的准确性不足，以及需要缓存大量相似性结果导致的算法可扩展性受限等缺点。为此提出了一种基于物品标签向量下的用户模糊偏好相似性度量方法，该方法使用模糊逻辑度量了不同用户对不同物品内容标签喜欢、不喜欢的程度，将用户间的相似性表示为物品内容标签上的一个向量。随后依据该向量与预测目标物品内容标签之间的关系，设计了相应的相似性计算和评分预测公式。在两个常用数据集上的实验结果表明，相较于其他算法该文在体现评分预测准确性指标平均绝对误差上提升了12.38%；在体现偏好预测准确性的F1值上提升了7.85%；在体现排序准确性的半衰期效用指标上提升了17.47%。同时，该文提出的算法减少了各指标的最优预测时需要的邻居数，缩短了算法运行时间，有效提升了可扩展性。
Traditional collaborative filtering algorithms suffer from various shortcomings such as insufficient accuracy due to rating prediction errors, and limited scalability of the algorithm due to the need to cache numerous similarity results. To address these challenges, this paper proposes a user fuzzy preference similarity measurement method based on item label vectors. The method uses fuzzy logic to measure the likes and dislikes of different users towards different item content labels, and represents the similarity between users as a vector across these labels. The corresponding similarity calculation and rating prediction formulas are then designed based on the relationship between this vector and the predicted target item content labels. Experimental results on two commonly used datasets demonstrate significant improvements over recent algorithms. Specifically, compared to existing methods, the proposed approach yields a 12.38% enhancement in mean absolute error, indicating improved rating prediction accuracy, a 7.85% increase in F1 value, indicating enhanced preference prediction accuracy, and a 17.47% improvement in half-life utility, reflecting sorting accuracy. Furthermore, the algorithm proposed in this paper reduces the number of neighbors required for the optimal prediction of each metric, thereby shortening the running time of the algorithm and effectively enhancing the scalability.
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