Jinfeng Zhuang

Modeling social strength in social media community via kernel-based learning

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.

A two-view learning approach for image tag ranking

Tags of social images play a central role for text-based social image retrieval and browsing tasks. However, the original tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In this paper, we aim to overcome the challenge of social tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the conventional learning approaches that usually assume some parametric models, our method is completely data-driven and makes no assumption of the underlying models, making the proposed solution practically more effective. We formally formulate our method as an optimization task and present an efficient algorithm to solve it. To evaluate the efficacy of our method, we conducted an extensive set of experiments by applying our technique to both text-based social image retrieval and automatic image annotation tasks, in which encouraging results showed that the proposed method is more effective than the conventional approaches.

SimpleNPKL: simple non-parametric kernel learning

Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity of standard interior-point SDP solvers could be as high as O(n6.5). Such intensive computation cost prohibits NPK learning applicable to real applications, even for data sets of moderate size. In this paper, we propose an efficient approach to NPK learning from side information, referred to as SimpleNPKL, which can efficiently learn non-parametric kernels from large sets of pairwise constraints. In particular, we show that the proposed SimpleNPKL with linear loss has a closed-form solution that can be simply computed by the Lanczos algorithm. Moreover, we show that the SimpleNPKL with square hinge loss can be re-formulated as a saddle-point optimization task, which can be further solved by a fast iterative algorithm. In contrast to the previous approaches, our empirical results show that our new technique achieves the same accuracy, but is significantly more efficient and scalable.

Non-parametric kernel ranking approach for social image retrieval

Social image retrieval has become an emerging research challenge in web rich media search. In this paper, we address the research problem of text-based social image retrieval, which aims to identify and return a set of relevant social images that are related to a text-based query from a corpus of social images. Regular approaches for social image retrieval simply adopt typical text-based image retrieval techniques to search for the relevant social images based on the associated tags, which may suffer from noisy tags. In this paper, we present a novel framework for social image re-ranking based on a non-parametric kernel learning technique, which explores both textual and visual contents of social images for improving the ranking performance in social image retrieval tasks. Unlike existing methods that often adopt some fixed parametric kernel function, our framework learns a non-parametric kernel matrix that can effectively encode the information from both visual and textual domains. Although the proposed learning scheme is transductive, we suggest some solution to handle unseen data by warping the non-parametric kernel space to some input kernel function. Encouraging experimental results on a real-world social image testbed exhibit the effectiveness of the proposed method.

On profiling blogs with representative entries

With an explosive growth of blogs, information seeking in blogosphere becomes more and more challenging. One example task is to find the most relevant topical blogs against a given query or an existing blog. Such a task requires concise representation of blogs for effective and efficient searching and matching. In this paper, we investigate a new problem of profiling a blog by choosing a set of m most representative entries from the blog, where m is a predefined number that is application-dependent. With the set of selected representative entries, applications on blogs avoid handling hundreds or even thousands of entries (or posts) associated with each blog, which are updated frequently and often noisy in nature. To guide the process of selecting the most representative entries, we propose three principles, i.e., anomaly, representativeness, and diversity. Based on these principles, a greedy yet very efficient entry selection algorithm is proposed. To evaluate the entry selection algorithms, an extrinsic evaluation methodology from document summarization research is adapted. Specifically, we evaluate the proposed entry selection algorithms by examining their blog classification accuracies. By evaluating on a number of different classification methods, our empirical results showed that comparable classification accuracy could be achieved by using fewer than 20 representative entries for each blog compared to that of engaging all entries.

Community Discovery from Social Media by Low-Rank Matrix Recovery

The pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective generating process, the explicitly observed communities (e.g., group-user and user-user relationship) are often noisy and incomplete in nature. This paper presents a novel approach to discovering communities from social media, including the group membership and user friend structure, by exploring a low-rank matrix recovery technique. In particular, we take Flickr as one exemplary social media platform. We first model the observed indicator matrix of the Flickr community as a summation of a low-rank true matrix and a sparse error matrix. We then formulate an optimization problem by regularizing the true matrix to coincide with the available rich context and content (i.e., photos and their associated tags). An iterative algorithm is developed to recover the true community indicator matrix. The proposed approach leads to a variety of social applications, including community visualization, interest group refinement, friend suggestion, and influential user identification. The evaluations on a large-scale testbed, consisting of 4,919 Flickr users, 1,467 interest groups, and over five million photos, show that our approach opens a new yet effective perspective to solve social network problems with sparse learning technique. Despite being focused on Flickr, our technique can be applied in any other social media community.

Representative entry selection for profiling blogs

Many applications on blog search and mining often meet the challenge of handling huge volume of blog data, in which one single blog could contain hundreds or even thousands of entries. We investigate novel techniques for profiling blogs by selecting a subset of representative entries for each blog. We propose two principles for guiding the entry selection task: representativeness and diversity. Further, we formulate the entry selection task into a combinatorial optimization problem and propose a greedy yet effective algorithm for finding a good approximate solution by exploiting the theory of submodular functions. We suggest blog classification for judging the performance of the proposed entry selection techniques and evaluate their performance on a real blog dataset, in which encouraging results were obtained.

Social Image Tag Ranking by Two-View Learning

Tags play a central role in text-based social image retrieval and browsing. However, the tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In order to solve this problem, researchers have proposed techniques to rank the annotated tags of a social image according to their relevance to the visual content of the image. In this paper, we aim to overcome the challenge of social image tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the conventional learning approaches that usually assumes some parametric models, our method is completely data-driven and makes no assumption about the underlying models, making the proposed solution practically more effective. We formulate our method as an optimization task and present an efficient algorithm to solve it. To evaluate the efficacy of our method, we conducted an extensive set of experiments by applying our technique to both text-based social image retrieval and automatic image annotation tasks. Our empirical results showed that the proposed method can be more effective than the conventional approaches.