【摘要】 [目的/意义]从在线健康社区用户兴趣的动态迁移性出发，将时间特征融入社交关系和个人偏好，完善在线健康社区个性化推荐算法，进一步提高用户获取健康信息的准确性。[方法/过程]首先，从用户社交关系出发，构建融入时间特征的用户影响关系网络；其次，依据用户个人偏好，构建融入时间特征的用户话题帖匹配矩阵；最后，将两者融合得到用户话题帖兴趣评分矩阵，据此形成每个用户的TOP-N推荐列表。[结果/结论]构建的融合时间特征的个性化推荐算法可提高推荐的准确度，提升在线健康社区个性化推荐算法的性能。更多还原

【Abstract】 [Purpose/Significance]The study starts from the dynamic migration of users’ interests in online health communities, temporal features, which are integrated into social relationships and personal preferences, so as to improve the personalized recommendation algorithm for online health communities and further improve the accuracy of users’ access to health information.[Methods/Process]Firstly, starting from users’ social relationships, the paper constructed a users’ influence relationships network fusing temporal features; Secondly, based on users’ personal preferences, it built a matching matrix of user topic posts which integrated temporal features; Finally, an interest rating matrix of user topic post was fused, from which the TOP-N recommendation list for each user was extracted.[Results/Conclusion]The constructed personalized recommendation algorithm with fused temporal features can improve the accuracy of recommendations and enhance the performance of the personalized recommendation algorithm for online health communities.更多还原