随着互联网技术的发展以及社交网络的扩大,网络平台已经成为人们获取信息的一个重要途径.标签的引入提升了信息分类及检索效率.同时,标签推荐系统的出现不仅方便了用户输入标签,还提高了标签的质量.传统的标签推荐算法通常只考虑标签和项目两个主体,而忽略了用户在选择标签时个人意图所起到的重要作用.由于在标签推荐系统中标签最终由用户确定,因此用户的偏好在标签推荐中起着关键作用.为此,引入用户作为主体,并结合用户发布的历史帖子的先后顺序,将标签推荐任务建模为更加符合真实场景的序列标签推荐任务.提出了一种基于MLP的序列标签推荐方法(MLP for Sequential Tag Recommendation,MLP4STR),该方法显式地建模用户偏好用于引导整体标签推荐.MLP4STR采用一种跨特征对齐的MLP序列特征提取框架,将文本和标签的特征对齐,获取用户的历史帖子信息和历史标签信息中隐含的用户动态兴趣.最后,结合帖子内容和用户偏好进行标签推荐.在4个真实世界的数据集上得到的实验结果表明,MLP4STR能够有效地学习序列标签推荐中的用户历史行为序列的信息,其中,评价指标F1@5较最优的对比算法有显著提升.
Sequential Tag Recommendation
With the development of Internet technology and the expansion of social networks,online platforms have become a sig-nificant avenue for people to access information.The introduction of tags has facilitated the categorization and retrieval of infor-mation.At the same time,the advent of tag recommendation systems not only makes it easier for users to input tags but also im-proves the quality of tags.Traditional tag recommendation algorithms typically only consider tags and items,overlooking the cru-cial role of personal intent when users choose tags.Since tags in a recommendation system are ultimately determined by users,user preferences play a key role in tag recommendation.Therefore,we introduce the user as a subject,and by incorporating the chronological order of users'historical posts,modeling the task of tag recommendation as a sequential tag recommendation task that is more aligned with real-world scenarios.To address this task,this paper proposes a method named MLP for sequential tag recommendation(MLP4STR),which explicitly models user preferences to guide the overall tag recommendation.MLP4STR em-ploys a cross-feature alignment MLP framework for sequence feature extraction,aligns the features of text and tags to capture the dynamic interests of users implicit in their historical post and tag information.Finally,it recommends tags by combining post con-tent and user preferences.Experimental results on four real-world datasets show that MLP4STR can effectively learn information from users'historical behavior sequences in sequential tag recommendation,and the evaluation metric F1@5 shows a significant improvement compared to the optimal baseline algorithms.
Tag recommendationSequential recommendationMulti-label learningUser preference
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