Multi-domain collaborative recommendation with feature selection
Multi-domain collaborative recommendation with feature selection
复制标题
DOI：
10.1109/cc.2017.8014374
复制
发表时间：
2017-08
影响因子：
4.1
通讯作者：
Lizhen Liu;Junjun Cui;Wei Song;Hanshi Wang
中科院分区：
文献类型：
--
作者：
Lizhen Liu;Junjun Cui;Wei Song;Hanshi Wang
协同过滤作为最流行的技术之一，在推荐系统中扮演着重要的角色。然而，当用户-项目评分矩阵稀疏时，其性能将退化。最近，已经开发了特定领域的推荐方法来解决这个问题。其基本思想是将用户和项目划分为重叠的域，然后在每个域中独立地执行推荐。这里，域指的是对一组产品具有相似偏好的一组用户。然而，这些由两个连续步骤组成的特定领域的方法忽略了领域分割和推荐的互惠互利。因此，本文提出了一个统一的框架来同时实现推荐和利用评分矩阵背后的领域信息。该模型基于矩阵分解，通过学习多个领域的用户偏好和偏好选择向量来为每组产品选择相关特征。此外，利用用户-项目评分矩阵中的局部上下文信息来增强新的框架。在CIAO和Epinions两个广泛使用的数据集上的实验结果证明了该模型的有效性。
Collaborative filtering, as one of the most popular techniques, plays an important role in recommendation systems. However, when the user-item rating matrix is sparse, its performance will be degenerate. Recently, domain-specific recommendation approaches have been developed to address this problem. The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefit of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization, the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework. Experimental results on two widely used data-sets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model.