Collaborative filtering recommendation algorithm based on user preference derived from item domain features
Collaborative filtering recommendation algorithm based on user preference derived from item domain features
复制标题
基于项目域特征的用户偏好的协同过滤推荐算法
DOI：
10.1016/j.physa.2013.11.013
复制
发表时间：
2014-02-15
影响因子：
3.3
通讯作者：
Liu, Che
中科院分区：
文献类型：
--
作者：
Zhang, Jing;Peng, Qinke;Liu, Che
个性化推荐是对抗“信息过载”的有效方法。然而，其性能往往受到稀疏性和冷启动等多种因素的限制。一些研究者利用社交标签系统中用户创建的标签来刻画用户偏好进行个性化推荐，但由于用户描述习惯的差异以及语言表达的多样性，很难识别出具有相似兴趣的用户。为了找到更好的方式来描述用户偏好，使其更适合个性化推荐，我们引入了一个利用项目域特征构建用户偏好模型的框架，并将这些模型与协同过滤（CF）相结合。该框架不仅将领域特征集成到个性化推荐中，而且有助于检测用户之间的隐含关系，而这是传统CF方法所遗漏的。实验结果表明我们的方法取得了更好的结果，证明了用户偏好模型对于推荐是更有效的。皇冠版权所有 (C) 2013 由 Elsevier B.V. 出版。保留所有权利。
Personalized recommendation is an effective method for fighting "information overload". However, its performance is often limited by several factors, such as sparsity and cold-start. Some researchers utilize user-created tags of social tagging system to depict user preferences for personalized recommendation, but it is difficult to identify users with similar interests due to the differences between users' descriptive habits and the diversity of language expression. In order to find a better way to depict user preferences to make it more suitable for personalized recommendation, we introduce a framework that utilizes item domain features to construct user preference models and combines these models with collaborative filtering (CF). The framework not only integrates domain characteristics into a personalized recommendation, but also aids to detecting the implicit relationships among users, which are missed by the conventional CF method. The experimental results show our method achieves the better result, and prove the user preference model is more effective for recommendation. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.