Social graphs, representing online friendships among users, are one of the fundamental types of data for many applications, such as recommendation, virality prediction and marketing in social media. However, this data may be unavailable due to the privacy concerns of users, or kept private by social network operators, which makes such applications difficult. Inferring users' interests and discovering users' connections through their shared multimedia content has attracted more and more attention in recent years. This paper proposes a Gaussian relational topic model for connection discovery using user shared images in social media. The proposed model not only models users' interests as latent variables through their shared images, but also considers the connections between users as a result of their shared images. It explicitly relates user shared images to user connections in a hierarchical, systematic and supervisory way and provides an end-to-end solution for the problem. This paper also derives efficient variational inference and learning algorithms for the posterior of the latent variables and model parameters. It is demonstrated through experiments with over 200k images from Flickr that the proposed method significantly outperforms the methods in previous works.
One content-based approach to discover connections through images is to generate a label for an image based on its visual elements @cite_2 @cite_9 . However, determining the relationship between the visual elements and the label is not a trivial task because the same object can be visually different among images, and denoting each image by a single label loses a lot of information. @cite_6 and @cite_4 propose to first generate labels for user shared images by clustering and describe users' characteristics by counting the occurrences of image clusters appearing in their collections. The prediction is then made through analyzing the similarities among the users' histograms. Both methods assume each image contains one and only one topic, neglecting potentially rich information. On the other hand, @cite_0 and @cite_8 represent users' interests by summing the feature vectors of their images, achieving a similar effect to the other methods. However, all these methods do not actually connect shared images to the links between users, or only connect shared images to links, and not the other way around.
@cite_4 @cite_8 @cite_9 @cite_6 @cite_0 @cite_2
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Social graphs, representing the online friendships among users, are one of the most fundamental types of data for many social media applications, such as recommendation, virality prediction and marketing. However, this data may be unavailable due to the privacy concerns of users, or kept privately by social network operators, which makes such applications difficult. One of the possible solutions to discover user connections is to use shared content, especially images on online social networks, such as Flickr and Instagram. This paper investigates how non-user generated labels annotated on shared images can be used for connection discovery with different color-based and feature-based methods. The label distribution is computed to represent users, and followee follower relationships are recommended based on the distribution similarity. These methods are evaluated with over 200k images from Flickr and it is proven that with non-user generated labels, user connections can be discovered, regardless of the method used. Feature-based methods are also proven to be 95 better than color-based methods, and 65 better than tag-based methods. Modeling continuous social strength rather than conventional binary social ties in the social network can lead to a more precise and informative description of social relationship among people. In this paper, we study the problem of social strength modeling (SSM) for the users in a social media community, who are typically associated with diverse form of data. In particular, we take Flickr---the most popular online photo sharing community---as an example, in which users are sharing their experiences through substantial amounts of multimodal contents (e.g., photos, tags, geo-locations, friend lists) and social behaviors (e.g., commenting and joining interest groups). Such heterogeneous data in Flickr bring opportunities yet challenges to the research community for SSM. One of the key issues in SSM is how to effectively explore the heterogeneous data and how to optimally combine them to measure the social strength. In this paper, we present a kernel-based learning to rank framework for inferring the social strength of Flickr users, which involves two learning stages. The first stage employs a kernel target alignment algorithm to integrate the heterogeneous data into a holistic similarity space. With the learned kernel, the second stage rectifies the pair-wise learning to rank approach to estimating the social strength. By learning the social strength graph, we are able to conduct collaborative recommendation and collective classification. The promising results show that the learning-based approach is effective for SSM. Despite being focused on Flickr, our technique can be applied to model social strength of users in any other social media community. Social image tagging is becoming increasingly popular with the development of social website, where images are annotated with arbitrary keywords called tags. Most of present image tagging approaches are mainly based on the visual similarity or mapping between visual feature and tags. However, in the social media environment, images are always associated with multi-type of object information (i.e., visual content, tags, and user contact information) which makes this task more challenging. In this paper, we propose to fuse multi-type of information to tag social image. Specifically, we model social image tagging as a ''ranking and reinforcement'' problem, and a novel graph-based reinforcement algorithm for interrelated multi-type objects is proposed. When a user issue a tagging request for a query image, a candidate tag set is derived and a set of friends of the query user is selected. Then a graph which contains three types of objects (i.e., visual features of the query image, candidate tags, and friend users) is constructed, and each type of objects are initially ranked based on their weight and intra-relation. Finally, candidate tags are re-ranked by our graph-based reinforcement algorithm which takes into consideration both inter-relation with visual features and friend users, and the top ranked tags are saved. Experiments on real-life dataset demonstrate that our algorithm significantly outperforms state-of-the-art algorithms. Billions of user-shared images are generated by individuals in many social networks today, and this particular form of user data is widely accessible to others due to the nature of online social sharing. When user social graphs are only accessible to exclusive parties, these user-shared images are proved to be an easier and effective alternative to discover user connections. This work investigated over 360 000 user shared images from two social networks, Skyrock and 163 Weibo, in which 3 million follower followee relationships are involved. It is observed that the shared images from users with a follower followee relationship show relatively higher similarities . A multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user- generated tags and social graphs for follower followee recommendation and gender identification. To the best of our knowledge, this is the first attempt in this field to prove and formulate such a phenomenon for mass user-shared images along with more practical prediction methods. These findings are useful for information or services recommendations in any social network with intensive image sharing, as well as for other interesting personalization applications, particularly when there is no access to those exclusive user social graphs. User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences. Large collaborative datasets offer the challenging opportunity of creating systems capable of extracting knowledge in the presence of noisy data. In this work we explore the ability to automatically learn tag semantics by mining a global georeferenced image collection crawled from Flickr with the aim of improving an automatic annotation system. We are able to categorize sets of tags as places, landmarks, and visual descriptors. By organizing our dataset of more than 1.69 million images using a quadtree we can efficiently find geographic areas with sufficient density to provide useful results for place and landmark extraction. Precision-recall curves for our techniques compared with previous existing work used to identify place tags and manual groundtruth landmark annotation show the merit of our methods applied on a world scale.