一种模糊认知的协同过滤算法

A collaborative filtering algorithm based on fuzzy cognition

协同过滤是目前电子商务推荐系统中应用最成功的个性化推荐技术之一,但传统的协同过滤算法认为各个时期的评分数据信息是静态的。针对该问题,提出两种模糊认知:评分的模糊递增和评分权重的模糊递增。首先,对项目的评分信息划分时间窗口,且利用链式结构计算项目的相似性,选择目标项目的最近邻居;其次,对评分数据赋予时间权重,提出一种权重函数,并对传统的预测方法进行改进。同时,在预测阶段提出一种分层式的优化策略对评分的时间权重进行求解,完成推荐。最后,在Netflix的数据集实验结果表明,该算法较传统的协同过滤算法有显著的提高,推荐准确率提升了9.8%~14.1%。

Collaborative filtering is one of the most successful personalized recommendation techniques currently used in E-commerce recommendation systems. However, traditional collaborative filtering algorithms assume the ratings to be static in each period. To solve this problem, two kinds of fuzzy cognition are proposed： fuzzy increasing of ratings and fuzzy increasing of time weights. Firstly, item ratings are divided into time windows, the similarity between items is calculated using a chain structure, and the nearest neighbors of the target item is selected. Secondly, time weights are assigned to ratings, a weight function is proposed, and the traditional prediction method is improved. At the same time, a hierarchical optimization strategy is proposed in the prediction phase to solve the time weights of rat ings, thus completing the recommendation. Finally, experiments on the Netflix datasets show that, compared with the traditional collaborative filtering algorithms, our proposal improves the recommenda tion accuracy by 9.8%-14.l%.