基于标签、得分和偏好时效性的项目推荐方法

网络信息的爆炸式增长使得推荐系统成为一项研究的热点。现存的推荐系统在实际运营中存在各自的缺陷。在web2.0环境下,标签、项目得分以及用户标注项目的时间均包含暗示用户偏好的重要信息,这些信息对提高推荐系统准确度是十分重要的。在借鉴协同过滤思想的基础上,提出综合考虑标签、项目得分和用户偏好时效性的项目推荐模型,并对此模型的体系结构及应用前景进行了分析。

Item Recommendation Method Based on Tag, Rating and Preference Timeliness

The explosive growth of network information system makes the recommendation become a research hotspot. Existing recommendation systems used in the actual operation have respective defects. In web2.0 environment, tags, the item rating and the time of the user tagging the item contain important information suggesting the user＇s preference. This information is useful to improve the accuracy of the recommendation system. Drawing on collaborative filtering method, we suggest an item recommendation model which is considering tags, the item rating and timeliness of user＇s preference all together and discuss the architecture and prospect of this method.