Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure

Extracting Relevance and Affect Information from Physiological Text Annotation

We present physiological text annotation, which refers to the practice of associating physiological responses to text content in order to infer characteristics of the user information needs and affective responses. Text annotation is a laborious task, and implicit feedback has been studied as a way to collect annotations without requiring any explicit action from the user. Previous work has explored behavioral signals, such as clicks or dwell time to automatically infer annotations, and physiological signals have mostly been explored for image or video content. We report on two experiments in which physiological text annotation is studied first to 1) indicate perceived relevance and then to 2) indicate affective responses of the users. The first experiment tackles the user’s perception of relevance of an information item, which is fundamental towards revealing the user’s information needs. The second experiment is then aimed at revealing the user’s affective responses towards a -relevant- text document. Results show that physiological user signals are associated with relevance and affect. In particular, electrodermal activity (EDA) was found to be different when users read relevant content than when they read irrelevant content and was found to be lower when reading texts with negative emotional content than when reading texts with neutral content. Together, the experiments show that physiological text annotation can provide valuable implicit inputs for personalized systems. We discuss how our findings help design personalized systems that can annotate digital content using human physiology without the need for any explicit user interaction

Explorative Analysis of Recommendations Through Interactive Visualization

Even though today's recommender algorithms are highly sophisticated, they can hardly take into account the users' situational needs. An obvious way to address this is to initially inquire the users' momentary preferences, but the users' inability to accurately state them upfront may lead to the loss of several good alternatives. Hence, this paper suggests to generate the recommendations without such additional input data from the users and let them interactively explore the recommended items on their own. To support this explorative analysis, a novel visualization tool based on treemaps is developed. The analysis of the prototype demonstrates that the interactive treemap visualization facilitates the users' comprehension of the big picture of available alternatives and the reasoning behind the recommendations. This helps the users get clear about their situational needs, inspect the most relevant recommendations in detail, and finally arrive at informed decisions

Near-infrared sensitivity enhancement of photorefractive polymer composites by pre-illumination

Among the various applications for reversible holographic storage media, a particularly interesting one is time-gated holographic imaging (TGHI). This technique could provide a noninvasive medical diagnosis tool, related to optical coherence tomography. In this technique, biological samples are illuminated within their transparency windowwith near-infrared light, and information about subsurface features is obtained by a detection method that distinguishes between reflected photons originating from a certain depth and those scattered from various depths. Such an application requires reversible holographic storage media with very high sensitivity in the near-infrared. Photorefractive materials, in particular certain amorphous organic systems, are in principle promising candidate media, but their sensitivity has so far been too low, mainly owing to their long response times in the near-infrared. Here we introduce an organic photorefractive material—a composite based on the poly(arylene vinylene) copolymer TPD-PPV—that exhibits favourable near-infrared characteristics. We show that pre-illumination of this material at a shorter wavelength before holographic recording improves the response time by a factor of 40. This process was found to be reversible. We demonstrate multiple holographic recording with this technique at video rate under practical conditions

A multidimensional evaluation framework for personal learning environments

Evaluating highly dynamic and heterogeneous Personal Learning Environments (PLEs) is extremely challenging. Components of PLEs are selected and configured by individual users based on their personal preferences, needs, and goals. Moreover, the systems usually evolve over time based on contextual opportunities and constraints. As such dynamic systems have no predefined configurations and user interfaces, traditional evaluation methods often fall short or are even inappropriate. Obviously, a host of factors influence the extent to which a PLE successfully supports a learner to achieve specific learning outcomes. We categorize such factors along four major dimensions: technological, organizational, psycho-pedagogical, and social. Each dimension is informed by relevant theoretical models (e.g., Information System Success Model, Community of Practice, self-regulated learning) and subsumes a set of metrics that can be assessed with a range of approaches. Among others, usability and user experience play an indispensable role in acceptance and diffusion of the innovative technologies exemplified by PLEs. Traditional quantitative and qualitative methods such as questionnaire and interview should be deployed alongside emergent ones such as learning analytics (e.g., context-aware metadata) and narrative-based methods. Crucial for maximal validity of the evaluation is the triangulation of empirical findings with multi-perspective (end-users, developers, and researchers), mixed-method (qualitative, quantitative) data sources. The framework utilizes a cyclic process to integrate findings across cases with a cross-case analysis in order to gain deeper insights into the intriguing questions of how and why PLEs work

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning