核心思想与思路: (把推荐的准确度和多样性框到监督学习模式下), 做法如下:
(1) 启发式构造ground-truth: 根据一定的准则, 人为选取一些item构成一个集合 (例如用户高评分的,彼此之间尽可能不相似的)
(2) 利用MF的思想, 学出U和I两个隐矩阵(这个基本上是连接其他model的桥梁), 需要的注意的是, 他比传统MF多了一个参数\sigma(对角矩阵, 即协相关系数): （不太明白这个参数的作用，难道只是为了起桥梁作用？）注意：这里并没用到(1)中得到的所谓的ground-truth
（３）利用(2)中学出的U,I以及\sigma还有(1)中的ground-truth, 以及随机组合的预测ground-truth, 我们就可以构造关于ground-truth和预测ground-truth的特征向量(文中指出的2k+4维度)
(4) 再利用(3)中得到的信息和一些关于函数损失的定义, 就可以把他们一起纳入structure SVM的框架中(有兴趣可以进一步了解), 这样就学出了structure SVM的参数W,以及(2)中的\sigma (可以反过来更新MF中的U和V)
(5) 有了W之后, 就可以直接计算预测ground-truth的相关值(是什么,不知道!), 反正值最大的最好,就作为推荐结果!
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Learning to Recommend Accurate and Diverse Items | |||||||||||
| 文献作者 | Peizhe Cheng; Shuaiqiang Wang | ||||||||||
| 文献发表年限 | 2017 | ||||||||||
| 文献关键字 | |||||||||||
| WWW; Diversity; Collaborative filtering; Recommender systems; Structural SVM | |||||||||||
| 摘要描述 | |||||||||||
| In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground- truth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled prob- lems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms. | |||||||||||
