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Learn to Personalized Image Search From the Photo Sharing Websites

机译：从照片共享网站学习个性化图像搜索

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摘要

Increasingly developed social sharing websites like Flickr and Youtube allow users to create, share, annotate, and comment medias. The large-scale user-generated metadata not only facilitate users in sharing and organizing multimedia content, but provide useful information to improve media retrieval and management. Personalized search serves as one of such examples where the web search experience is improved by generating the returned list according to the modified user search intents. In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related search intent into user-specific topic spaces. Since the users' original annotation is too sparse for topic modeling, we need to enrich users' annotation pool before user-specific topic spaces construction. The proposed framework contains two components: 1) a ranking-based multicorrelation tensor factorization model is proposed to perform annotation prediction, which is considered as users' potential annotations for the images; 2) we introduce user-specific topic modeling to map the query relevance and user preference into the same user-specific topic space. For performance evaluation, two resources involved with users' social activities are employed. Experiments on a large-scale Flickr dataset demonstrate the effectiveness of the proposed method.
机译：越来越多的社交共享网站（例如Flickr和Youtube）允许用户创建，共享，注释和评论媒体。用户生成的大规模元数据不仅可以帮助用户共享和组织多媒体内容，而且可以提供有用的信息来改善媒体的检索和管理。个性化搜索用作此类示例之一，其中通过根据修改的用户搜索意图生成返回列表来改善Web搜索体验。在本文中，我们利用社交注释并同时考虑用户和查询的相关性，提出了一个新颖的框架，以学习个性化图像搜索。基本前提是将用户首选项和与查询相关的搜索意图嵌入到用户特定的主题空间中。由于用户的原始注释对于主题建模而言太稀疏，因此在构建用户特定的主题空间之前，我们需要丰富用户的注释池。所提出的框架包括两个部分：1）提出了一种基于等级的多相关张量因子分解模型来进行标注预测，该模型被视为用户对图像的潜在标注； 2）我们引入了特定于用户的主题建模，以将查询相关性和用户偏好映射到相同的特定于用户的主题空间中。为了进行绩效评估，使用了与用户的社交活动有关的两种资源。在大规模Flickr数据集上的实验证明了该方法的有效性。

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