Recommendation has become one of the most important components of online services for improving sale records, however visualization work for online recommendation is still very limited. This paper presents an interactive recommendation approach with the following two components. First, rating records are the most widely used data for online recommendation, but they are often processed in high-dimensional spaces that can not be easily understood or interacted with. We propose a Latent Semantic Model (LSM) that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. Our approach emphasizes interactivity, explicit user input, and semantic information convey; thus it can be used by general users without any knowledge of recommendation or visualization algorithms. We validate our model with data statistics and demonstrate our approach with case studies from the MovieLens100K dataset. Our approaches of latent semantic analysis and interactive recommendation can also be extended to other network-based visualization applications, including various online recommendation systems.
Several interactive or automatic storytelling approaches have been developed for applications ranging from general visualization process @cite_3 @cite_7 to specific domains. For example, Wohlfart and Hauser @cite_23 used storytelling as a guided and interactive visualization presentation approach for medical visualization. @cite_28 detected geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. @cite_39 generated automatic animations with narrative structures extracted from event graphs for time-varying scientific visualization. @cite_17 presented a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. @cite_10 presented a storytelling process with steps involved in finding insights, turning insights into a narrative, and communicating to an audience. Satyanarayan and Heer @cite_18 developed a model of storytelling abstractions and instantiate the model in Ellipsis with a graphical interface for story authoring. presented a narrative visualization system that presents literature review as interactive slides with three levels of narrative structures @cite_31 . @cite_12 generated textual annotations with a temporal layout and comic strip-style data snapshots for visualizing multidimensional and time-varying data.
@cite_18 @cite_7 @cite_28 @cite_3 @cite_39 @cite_23 @cite_31 @cite_10 @cite_12 @cite_17
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Data visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. Informed by interviews with journalists, we introduce a model of storytelling abstractions that includes state-based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain-specific language DSL for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award-winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives. A story is a powerful abstraction used by intelligence analysts to conceptualize threats and understand patterns as part of the analytical process. This paper demonstrates a system that detects geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. The GeoTime geo-temporal event visualization tool was augmented with a story system that uses narratives, hypertext linked visualizations, visual annotations, and pattern detection to create an environment for analytic exploration and communication, thereby assisting the analyst in identifying, extracting, arranging and presenting stories within the data The story system lets analysts operate at the story level with higher-level abstractions of data, such as behaviors and events, while staying connected to the evidence. The story system was developed and evaluated in collaboration with analysts. The paper address creating stories from data fabulas using computer graphics as a narrative medium. This approach aims to build various stories conveying the same fabula from a given dataset.. This paper presents a digital storytelling approach that generates automatic animations for time-varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree-like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time-varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time-varying data and easy animation generation for users without special knowledge about the underlying visualization techniques. In this paper we present a novel approach to volume visualization for presentation purposes that improves both the comprehensibility and credibility of the intended visualization message. Therefore, we combine selected aspects from storytelling as well as from interactive volume visualization to create a guided but at the same time interactive visualization presentation approach. To ease the observer's access to a presented visualization result we not only communicate the result itself, but also deliver its creational process in the form of an annotated visualization animation, which we call a visualization story. Additionally, we enable variable means of interactivity during story playback. The story observers may just watch the presentation passively, but they are also allowed to reinvestigate the visualization independently from story guidance, offering the ability to verify, confirm, or even disapprove the presented visualization message. For demonstration purposes, we developed a prototype application that provides tools to author, edit, and watch visualization stories. We demonstrate the potential of our approach on the basis of medical visualization examples. Reading academic paper is a daily task for researchers and graduate students. However, reading effectively can be challenging, particularly for novices in scientific research. For example, when readers are reading the related work section that cites a fair number of references in limited page space, they often need to flip back and forth between the text and the references and may also frequently search elsewhere for more information about the references. This increases the difficulty of understanding a paper. In this paper, we propose a narrative visualization system that helps the reading of academic papers. As a first step, we adopt narrative visualization to present literature review as interactive slides. Specifically, we propose a narrative structure with three levels of granularities that the reader can drill down or roll up freely. The logic flow of a slideshow can be organized based on the paper's presentation or citations. We demonstrate the effectiveness of our system through several case studies and user studies. The results show that the system allows users to quickly track and glance related work, making paper reading more effective and enjoyable. Presenting and communicating insights to an audience-telling a story-is one of the main goals of data exploration. Even though visualization as a storytelling medium has recently begun to gain attention, storytelling is still underexplored in information visualization and little research has been done to help people tell their stories with data. To create a new, more engaging form of storytelling with data, we leverage and extend the narrative storytelling attributes of whiteboard animation with pen and touch interactions. We present SketchStory, a data-enabled digital whiteboard that facilitates the creation of personalized and expressive data charts quickly and easily. SketchStory recognizes a small set of sketch gestures for chart invocation, and automatically completes charts by synthesizing the visuals from the presenter-provided example icon and binding them to the underlying data. Furthermore, SketchStory allows the presenter to move and resize the completed data charts with touch, and filter the underlying data to facilitate interactive exploration. We conducted a controlled experiment for both audiences and presenters to compare SketchStory with a traditional presentation system, Microsoft PowerPoint. Results show that the audience is more engaged by presentations done with SketchStory than PowerPoint. Eighteen out of 24 audience participants preferred SketchStory to PowerPoint. Four out of five presenter participants also favored SketchStory despite the extra effort required for presentation. Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets. Conveying a narrative with visualizations often requires choosing an order in which to present visualizations. While evidence exists that narrative sequencing in traditional stories can affect comprehension and memory, little is known about how sequencing choices affect narrative visualization. We consider the forms and reactions to sequencing in narrative visualization presentations to provide a deeper understanding with a focus on linear, 'slideshow-style' presentations. We conduct a qualitative analysis of 42 professional narrative visualizations to gain empirical knowledge on the forms that structure and sequence take. Based on the results of this study we propose a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. Our approach identifies possible transitions in a visualization set and prioritizes local (visualization-to-visualization) transitions based on an objective function that minimizes the cost of transitions from the audience perspective. We conduct two studies to validate this function. We also expand the approach with additional knowledge of user preferences for different types of local transitions and the effects of global sequencing strategies on memory, preference, and comprehension. Our results include a relative ranking of types of visualization transitions by the audience perspective and support for memory and subjective rating benefits of visualization sequences that use parallelism as a structural device. We discuss how these insights can guide the design of narrative visualization and systems that support optimization of visualization sequence.