【摘要】 标签是一种可以有效缓解数据稀疏问题的辅助信息,在个性化推荐研究中受到广泛关注。研究者们致力于结合标签信息挖掘出用户的行为偏好特征以及物品间隐含的语义关系,从而更好地产生推荐。本文从利用深度学习方法为被推荐对象(图像、文本等)推荐标签与从已有标签信息中提取用户、物品特征两个角度出发,通过对当前国内外相关文献分析基础上,指出了现有研究方法的优点与不足,最后提出了基于深度学习标签推荐未来主要研究方向。更多还原

【Abstract】 Tag is the assistant information that can effectively alleviate the problem of data sparse, and it has received great attention in personalized recommendation research. Based on tag information, researchers work at mining of users′ behavioral preferences and implicit semantic relationships between items, so as to better generate recommendations. Based on the analysis of relevant literature at home and abroad, this paper points out the advantages and disadvantages of the existing research methods from the perspectives of recommending tags for recommended objects(images, texts, etc.). Finally, the major research directions of deep learning tag-based recommendation in the future are put forward.更多还原