Due to the prevalence of social media service, effective and efficient online image retrieval is in urgent need to satisfy diversified requirements of Web users. Previous studies are mainly focusing on bridging the semantic gap by well-established content modeling with semantic information and social tagging information, but they are not flexible in aggregating the diversified expectations of the online users. In this paper, we present OSIR, a solution framework to facilitate the diversified preference styles in online social media image searching by textual query inputs. First, we propose an efficient Online Multiple Kernel Ranking (OMKR) model which is constructed on multiple query dimensions and complimentary feature channels, and trained by minimizing the triplet loss on hard negative samples. By optimizing the ranking performance with multi-dimensional queries, the semantic consistency between the image ranking and textual query input is directly maximized without relying on the intermediate semantic annotation procedure. Second, we construct random walk-based preference modeling by domain-specific similarity calculation on heterogeneous social attributes. By re-ranking the rank output of OMKR based on each preference ranking model, we obtain a set of ranking lists encoding different potential aspects of user preference. Last, we propose an effective and efficient position-sensitive rank aggregation approach to aggregate multiple ranking results based on the user preference specification. Extensive experiment on two social media datasets demonstrates the advantages of our approach in both retrieval performance and user experience.

由于社交媒体服务的普及，迫切需要有效，高效的在线图像检索，以满足Web用户的多样化需求。先前的研究主要集中在通过建立完善的具有语义信息和社交标签信息的内容建模来弥合语义鸿沟，但是它们在汇总在线用户的多样化期望方面并不灵活。在本文中，我们提出了OSIR，这是一个通过文本查询输入来促进在线社交媒体图像搜索中多样化偏好样式的解决方案框架。首先，我们提出了一种有效的在线多核排名（OMKR）模型，该模型建立在多个查询维度和互补特征通道上，并通过最小化硬性阴性样本的三元组损失进行训练。通过使用多维查询优化排名性能，无需依赖中间语义注释过程即可直接最大化图像排名和文本查询输入之间的语义一致性。其次，我们通过对异类社会属性进行特定领域的相似性计算，构建了基于随机游动的偏好建模。通过基于每个偏好排名模型对OMKR的排名输出进行重新排名，我们获得了一组编码用户偏好的不同潜在方面的排名列表。最后，我们提出了一种有效且高效的位置敏感排名聚合方法，以基于用户偏好规范来聚合多个排名结果。在两个社交媒体数据集上的广泛实验证明了我们的方法在检索性能和用户体验方面的优势。