Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations

Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches

Exploiting Social Annotation for Automatic Resource Discovery

Information integration applications, such as mediators or mashups, that require access to information resources currently rely on users manually discovering and integrating them in the application. Manual resource discovery is a slow process, requiring the user to sift through results obtained via keyword-based search. Although search methods have advanced to include evidence from document contents, its metadata and the contents and link structure of the referring pages, they still do not adequately cover information sources -- often called ``the hidden Web''-- that dynamically generate documents in response to a query. The recently popular social bookmarking sites, which allow users to annotate and share metadata about various information sources, provide rich evidence for resource discovery. In this paper, we describe a probabilistic model of the user annotation process in a social bookmarking system del.icio.us. We then use the model to automatically find resources relevant to a particular information domain. Our experimental results on data obtained from \emph{del.icio.us} show this approach as a promising method for helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on the We

Predictive Modeling for Navigating Social Media

Social media changes the way people use the Web. It has transformed ordinary Web users from information consumers to content contributors. One popular form of content contribution is social tagging, in which users assign tags to Web resources. By the collective efforts of the social tagging community, a new information space has been created for information navigation. Navigation allows serendipitous discovery of information by examining the information objects linked to one another in the social tagging space. In this dissertation, we study prediction tasks that facilitate navigation in social tagging systems. For social tagging systems to meet complex navigation needs of users, two issues are fundamental, namely link sparseness and object selection. Link sparseness is observed for many resources that are untagged or inadequately tagged, hindering navigation to the resources. Object selection is concerned when there are a large number of information objects that are linked to the current object, requiring to select the more interesting or relevant ones for guiding navigation effectively. This dissertation focuses on three dimensions, namely the semantic, social and temporal dimensions, to address link sparseness and object selection. To address link sparseness, we study the task of tag prediction. This task aims to enrich tags for the untagged or inadequately tagged resources, such that the predicted tags can serve as navigable links to these resources. For this task, we take a topic modeling approach to exploit the latent semantic relationships between resource content and tags. To address object selection, we study the task of personalized tag recommendation and trend discovery using social annotations. Personalized tag recommendation leverages the collective wisdom from the social tagging community to recommend tags that are semantically relevant to the target resource, while being tailored to the tagging preferences of individual users. For this task, we propose a probabilistic framework which leverages the implicit social links between like-minded users, i.e. who show similar tagging preferences, to recommend suitable tags. Social tags capture the interest of the users in the annotated resources at different times. These social annotations allow us to construct temporal profiles for the annotated resources. By analyzing these temporal profiles, we unveil the non-trivial temporal trends of the annotated resources, which provide novel metrics for selecting relevant and interesting resources for guiding navigation. For trend discovery using social annotations, we propose a trend discovery process which enables us to analyze trends for a multitude of semantics encapsulated in the temporal profiles of the annotated resources

Automated Social Text Annotation With Joint Multilabel Attention Networks

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d ) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study

Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

CFUI: Collaborative Filtering With Unlabeled Items

As opposed to Web search, social tagging can be considered an alternative technique tapping into the wisdom of the crowd for organizing and discovering information on the Web. Effective tag-based recommendation of information items is critical to the success of this social information discovery mechanism. Over the past few years, there have been a growing number of studies aiming at improving the item recommendation quality of collaborative filtering (CF) methods by leveraging tagging information. However, a critical problem that often severely undermines the performance of tag-based CF methods, i.e., sparsity of user-item and user-tag interactions, is still yet to be adequately addressed. In this paper, we propose a novel learning framework, which deals with this data sparsity problem by making effective use of unlabeled items and propagating users’ preference information between the item space and the tag space. Empirical evaluation using real-world tagging data demonstrates the utility of the proposed framework