Weighted-Tau Rank:一种采用加权Kendall Tau的面向排序的协同过滤算法

Weighted-Tau Rank:a Ranking-Oriented Algorithm for Collaborative Filtering

已知的面向排序的协同过滤算法主要有两个缺点:计算用户相似度时只考虑用户对同一产品对的偏好是否一致,而忽略了用户对产品对的偏好程度以及该偏好在用户间的流行度;进行偏好融合和排序时需要中间步骤来构建价值函数然后才能利用贪婪算法产生推荐列表。为解决上述问题:我们利用类TF-IDF加权策略对用户的偏好程度及偏好流行度进行综合考量,使用加权的Kendall Tau相关系数计算用户间的相似度;进行偏好融合与排序时则使用基于投票的舒尔茨方法直接产生推荐列表。在两个电影数据集上,本文提出的算法在评测指标NDCG上的效果要明显优于其他流行的协同过滤算法。

Most ranking oriented collaborative filtering （CF） algorithms have two limitations. Firstly, they only con- sider the accordance of user preferences but ignore the degrees and popularities of user preferences when computing user similarities. Secondly, an intermediate step is necessary to formulate the value function, for preference predic- tion and aggregation in greedy algorithms to derive recommendation lists. To address these problems, we propose a Degree-Popularity weighting scheme integrating TF-IDF to weight the degrees and popularities of user pairwise rela- tive preferences, and compute similarities between users based on weighted Kendall Tau rank correlation coefficient. Preference aggregations and predictions are directly formulated and the recommendation lists are consequently de- rived by applying the Schulze method. We conduct extensive experiments on two movie datasets under NDCG evalu- ation, implying advantageous results in comparison with the state-of-the-art CF algorithms.