P2P环境下基于社会化标签的个性化推荐模型研究

Model for Personalized Recommendation Based on Social Tagging in P2P Environment

【目的】利用用户使用标签的频率和时间因素计算用户的标签偏好向量,讨论用户兴趣的动态变化性对个性化推荐准确性的影响。【方法】构建P2P环境下基于社会化标签的个性化推荐模型,详细说明用户偏好的计算过程及推荐流程,并以西安某高校的P2P电影分享系统为对象进行实验验证。【结果】在随机选择的10名目标用户中,对其中8名用户的推荐命中率均高于传统基于用户评分的协同过滤推荐,说明综合用户标签使用频率和时间因素的推荐效果的优越性。【局限】由于本文主要研究用户兴趣的动态性对个性化推荐的影响,因此只在实验时人工删除无意义标签、合并相似标签,并没有引入有效的控制标签模糊性机制。【结论】在个性化推荐中,考虑用户兴趣的动态变化性,有助于提高推荐结果的准确性。

[Objective] Utilizing tags frequency and time used by the user, discussing the impact of dynamic changes of user interest for personalized recmmendation accuracy. [Methods] Constructing model for personalized recom- mendation based on social tagging in P2P environment, illustrating the calculation of user preferences and recommended process in detail. Making an experiment to verify the validity of the model using P2P movie sharing system. [Results] In 10 randomly selected target users, the hit rate of recommendation for eight users is higher than traditonal collabrative filtering which is based on scores, proving the advantages of making full use of tag frequency and time factor to recommend. [Limitations] Due to the main task of this paper is to reseach the impact of dynamic changes of user interst for personalized recommendation, so only delete meaningless tags and merge similar tags by hands, do not have an effective mechanism to control the ambiguity of tags. [Conclusions] Considering the dynamic changes of user interest can help to improve the accuracy of personalized recommendation.